Saturday night at Border’s Bookstore in Birmingham, MI, I saw at least three Glenn Beck ravings, Sarah Palin’s new “Roguely Ignorant,” one of Ann Coulter’s vile spewings and other droppings by lesser known wingnuts in the front displays, just as you enter the store. This was in the new hardcover and softcover book area. It was a pretty depressing site in a place where I’m usually happy to be.

These authors appeal to an unfortunately ignorant portion of the populace, which nicely explains why quality public education is so rabidly opposed by those on the far right. Frankly, these pundits and their followers frighten me. They resemble harbingers of a potential new dark ages and a revised Crusades designed to support bigoted, theocratic and, ultimately, ‘end times prophecy’ doomsday goals; or at least the appeal of these concepts to the pundits’ readers. I suspect their followers are filled with hate for ‘the others’ …(i.e., anyone who disagrees with them about pretty much anything, looks different, talks differently, is French, is educated, is not Christian, is not of European descent (other than French) or a True American,^TM eats scones rather than biscuits, calls Canadian bacon ham, doesn’t own a gun, has a passport, has alternative marriage arrangements, is a vegetarian, can speak more than one language, or understands sciency stuff; the list is endless really)… and against any change, even for the quantifiable betterment of everyone, especially themselves, due to fear, insecurity, and overcompensation for their own failures and shortcomings, real or imagined.

It is important to somehow educate these people and help them quit being afraid. Yes, sometimes I’m a hopeless optimist and slightly irrational about our ability to change things for the better. As a kid I thought we were on a trajectory of progress, now I realize it’s more like a scatterplot with a really low R² value. We need better education and an increase in rational thought so people can understand when they are being exploited by the rich and powerful, for the benefit of those rich and powerful, and against their own self-interest. Otherwise we will always be taking five steps forward followed by 4.999 steps backwards. It shouldn’t require a freaking paradigm shift or a war to make even the slightest progress in technological and humanitarian terms.

Sure, it is harder to write a book, article, or editorial that is based on the truth than it is to just go off on a raving rant (such as this blog post, for example). This alone puts us at a disadvantage relative to wingnuts who are appealing almost entirely to emotion; fact checking and accuracy take time, as does putting complex concepts into simple terms, but it is important to make the effort. I offer the following comment, which was in response to my tweet about the bookstore experience, as an example of why:

Comment:
Or maybe they want to stop left wing radicals from taking over our country, our lives, our health care, our money etc.

No, I’m going to stick with repulsive as a description of these authors and pundits. If you actually look at the facts, not the spin, and check those facts you will find that they distort the truth freely and fluently. Fox News actually won a court case giving them the right to lie on the air; facts need not apply. Certainly the same freedom applies to books. They operate on the assumption that their readers and listeners will NOT check up on them and, for the most part, they don’t. Even if not an outright lie, their writings are often related to passages taken out of context, or in the dim gray light of revisionist history.

These pundits appeal to the lowest common denominators of fear and racism present in the populace to peddle their drivel. They maximize their appeal to those who want to believe that their problems are caused by everyone but themselves. The Becks, Limbaughs, etc., of the world make a very good living by reinforcing those beliefs. Thankfully that demographic of the population seems to be down to about 30%. Those 30% also seem to approximate the baseline in our society for authoritarian behavior, the bulk of them needing a father figure (such as Limbaugh, Beck, a Pastor etc.) to be an authority and tell them what to think and do, and a much smaller number being the percentage who get rich off the rest of them (Limbaugh, Beck, O’Reilly, Cheney, etc.). Really, it’s just so much easier to be told what to think and what to do than it is to figure it out for yourself. Having a following like this is shooting ducks in a barrel for these pundits (it’s easier to hit ducks in a barrel than fish in a barrel).

Their audience seems to have already forgotten that it was George W. Bush who took a budget surplus and in eight years nearly bankrupted our country, not to mention a couple of little wars that he started. If all that you watch on TV are the wailing morons on Fox News, or theirs are the only books that you read, you will probably begin to believe all this bad stuff only happened during the past year. You likely won’t hear, or read, that the economy is starting to turn slightly around and that this is happening under a President who is hardly left wing. Actually, President Obama seems to be middle of the road on social issues and fairly conservative on economic issues. Nevertheless, the fans of these pundits, such as our commenter most likely, seem to think that somehow Obama is both a Socialist and Fascist simultaneously and/or a Manchurian Candidate imported long ago from some country they never knew existed. And, by the way, Hawaii isn’t a REAL state even if he was born there, which no one can ever prove to their satisfaction short of transporting them one by one back in time to the delivery room.

They don’t seem to realize that it isn’t just their country, lives, and money; those things belong to all of us. They also have already forgotten that it was President G.W. Bush who looted the treasury for the rich and that, at least, President Obama’s stimulus money is going back into the economy rather than being stored in offshore tax havens or handed out as gigantic bonuses to the very people who wrecked the economy in the first place.

We will never hear anything reasonable or responsible from these pundits either in their writings, TV appearances, or online missives. More importantly, neither will that 30%. That isn’t how these wingnuts make their money and they won’t give that up until people are educated enough to see them for what they are. It is up to rational progressives and freethinkers to get busy and write, talk and fight against these regressive anti-democratic, anti-reality memes. Get busy; and, by the way, put some emotion into it!

I haven’t seen my friend for decades, I graduated in 1968, he in 1969. I figured he was religious, he still lives in Oklahoma and there are indications in his profile, but it turns out that his friends are zealots indeed and seemingly immune to anything other than Christian dogma. They don’t seem to have a chink in their imagined armor of faith where reason can intrude. It will be interesting to see whether my friend (Cap’n Curtis) keeps me on board or makes me walk the plank.

To keep these folks anonymous, I haven’t used the full names of anyone but myself (ChemBob) and Steve Harvey. I’ve also embedded the video, so you can see for yourself what got this conversation going. It is clear, as usual in discussions with fundamentalists, that I’m not making any headway. My arguments might not be the best that could be used, but I’m not convinced that better arguments would matter either. Note the smug “holier than thou” Christianity displayed by ‘Phil’ after I’ve left the conversation.

In a very real sense, I feel sorry for these people. They will live their entire lives believing in a fictitious supernatural being that is watching over them and providing them with an afterlife provided they behave in a specific manner, support certain causes, and convert others to their way of thinking. I think this opens them up to all kinds of authoritarian dogma and ready manipulation by sociopaths in Christian guise. It also seems such a waste of time and energy.

Cap’n Curtis worth watching!
How Would you Introduce God?

Steve Harvey, secular comedian, in front of a secular audience. He poses the question “How would you introduce God?” His response is amazing. Takes 3 minutes to watch. Well worth it.

ChemBob
Rampant emotionalism doesn’t equate with rationality or reality. It is interesting how he’s using the faith of these people to promote his own monetary well-being and popularity. BTW, he’s hardly a secular comedian any more.

Richard
Enjoyed the enthusiasm in the cliip. Frankly there was a time I thought like Robert, but I spent 3 years intensly studying the Bible and the claims of Chrisitianity and came to the conclusion that while impossible to disprove, those claims made more sense than anything else I could come up with. In the end, I concluded belief and unbelief are matters of faith, and for me belief made more sense to me. Christianity is neither rampant emotionalism nor a lack of rationality, it is faith. Reality only extends as far as our senses can reach and then we are on our own. Denying something exists on the sole basis that one has not yet personally experienced it is true irrationality.

ChemBob
I was religious for a very long time. I’ve read and studied the entire Bible too (along with other religions). Parts of it are horrid and precisely what one would expect from bronze-age tribal feudalism. Even the New Testament is irrational in the concept that God sacrifices himself as his only son to give us mortals a way to be forgiven for a ridiculous original sin (involving talking snakes and a betrayal, which an omniscient God would have known would happen), then resurrects himself three days later. If you are a God, you kill yourself, and you know you aren’t actually dying, that you’re coming back, that’s not really much of a sacrifice is it? This is a tale of blood sacrifice and rebirth, common among primitive tribes, and its reality is desired by many because they fear death equaling nonexistence.

Actually we have made great strides in understanding the natural processes that have resulted in things on Earth being how they are. God is not required, not excluded, but not required. BTW, not believing in something for which there is no evidence is the rational, not the irrational choice. The burden of proof is on the claimant. We have developed instruments and mathematics that have extended our understanding beyond our senses. Such observations and measurements, operating within the scientific method, comprise the best means we have developed to understand reality.

Phil
Faith is the key — A belief in that which cannot be scientifically proven. Each person makes the choice. The path is narrow… You get to choose — Yea!!!!!!!!!!

Cap’n Curtis
Robert, Faith can not be rationalized. How can you be “Religious” with out faith? You have put your faith in your “science”. But your science AKA the laws of physics were set in motion by God. “In the beginning there was the Word. And the Word was with God. And the Word was God.”

ChemBob
I don’t have faith in science, not blind faith anyway. As a methodological approach, science simply works. Check out how we are communicating at this moment. It works. Now pray to God that I hear your thoughts and see if I do. All tests of intercessory prayer have failed to show cause and effect. Like I said, I’m not totally excluding the possibility of a God, but there is absolutely no evidence whatsoever that anything was set in motion by a supernatural being and lots of evidence of comprehensible natural mechanisms for everything around us. I don’t have faith in millennia old books written during a time where most things were not understood, other than through superstitions that had been passed down.

You are religious and that is fine, as long as you are not among those who would turn this country into a theocracy; it is clear how well that has worked in the Middle East. I am not religious and that is fine also.

Phil
Robert we will pray for that, but GOD gives you free will. Therefore, in my humble opinion, GOD can’t give you what you are not seeking and do not want. A few Christians can’t get together and direct your life like a puppet. If you would pray for the same thing earnestly — now were talking.

ChemBob
You don’t get it. I’m not asking you to pray for me, I neither want your prayers nor believe in prayer other than as a relaxing meditative device. My point was that it doesn’t work, that you could pray for me to hear your thoughts and that would not work, whereas via science and technology you can express and I can read your thoughts on FB. I’ve been where you are, the best cure for it is actually reading the Bible. Don’t even go into the entire question of free will, it’s too complicated for writing about in a FB comment.

Phil
I could not have said it better myself Robert — I don’t get it. But thats OK — I don’t need to. –

Kim
1 Cor 1 – For the message of the cross is foolishness to those who are perishing, but to us who are being saved it is the power of God. 19 For it is written: “I will destroy the wisdom of the wise; the intelligence of the intelligent I will frustrate.” 20 Where is the wise man? Where is the scholar? Where is the philosopher of this age? Has not God made foolish the wisdom of the world? 21 For since in the wisdom of God the world through its wisdom did not know him, God was pleased through the foolishness of what was preached to save those who believe. 22 Jews demand miraculous signs and Greeks look for wisdom, 23 but we preach Christ crucified: a stumbling block to Jews and foolishness to Gentiles, 24 but to those whom God has called, both Jews and Greeks, Christ the power of God and the wisdom of God. 25 For the foolishness of God is wiser than man’s wisdom, and the weakness of God is stronger than man’s strength.

Kim
I have many things I would love to say on this subject, but I need to go to bed. But just one thing…God does answer prayer. I have seen it many, many times. But God will not respond to tests.

Phil Scott
Nice Job Kim

ChemBob
Quoting an ancient book of the stories that mostly uneducated shepherds used to understand the world, along with your belief in your own personal experiences, which are impossible to separate statistically from coincidence, doesn’t make it true Kim. If anything does destroy our society, it will be the foolishness of blind religious zealotry, both Islamic and Christian, it won’t be the atheists and agnostics.

The only reason you are a Christian is because you were born into it. Had you been born in Saudi Arabia you would be Muslim; India, likely Hindu; ancient Greece you’d worship the ancient Gods of Olympus. I can’t comprehend why so many people can’t climb over the wall of their childhood religious indoctrination and see the light on the other side.

Your faith leaves you in the thrall of authoritarian figures, who can use their “authority” to manipulate you in ways that are not in your self-interest. Suicide bombers and doctor murderers are examples of that. They are also examples of extreme ignorance, stupidity, and the inability to think rationally. Fortunately most Christians and Muslims aren’t quite that radically enthralled, but there are many lesser things that religion coerces one to do that are still not to your benefit. It is time to wake up.

ChemBob
Kim, what I think you’re missing from my previous comments is that I’ve been there and done that, all of it. Baptist churches, Episcopal churches, attended services at several others, went to Bible schools, attended prayer groups, read the entire Bible, studied Zen, Taoism, the archaeology of religious sites, on and on and on. And I finally realized that there was absolutely nothing to indicate that there was any God anywhere other than in the imaginations of people. What I found, on the contrary, was lots of evidence that there is almost certainly no God.

If you read, really read the Bible, especially the extremely unpleasant parts (there are many), you will realize that you can’t possibly think that the actions and doings being described are those of an omniscient, omnipotent, omnipresent God. Much of it is entirely too petty and vain. Much of it is contradictory in the extreme. Much of it is downright silly and no rational person could believe that it would happen other than in drug or illness-induced hallucination.

You are certainly free to worship as you wish, as I am free to disbelieve. All I ask is that, within such faith, Christians (and Muslims for that matter) remain rational with regard to our humanity and the potential negative impacts that any extreme faith-based positions can have on their fellow humans. I also think that it is very important, both for the good of believers and non-believers, for religion and government to remain absolutely separate. After all, what if a variant of Christianity other than your own began using the government to dictate to you how you must believe?

Kim
Your other point that I would challenge is that because Christ is God and knew he would be resurrected His sacrifice is meaningless.

Christ had to undergo the cross in our weak human body. He felt the pain, he felt the loneliness, he felt the shame. He was abandoned by His friends; He was humiliated and spat upon by the people He came to save. He suffered torturous abuse, abuse that our minds cannot even really wrap themselves around. And He took on the weight of all of our sin. Sin so ugly that even God, His beloved Father, had to turn away from Him. And He did this all for us. So that we can be free from the punishment for our sins. And we know that Jesus agonized over the trial that He was going to undergo because the Bible tells us so in Matthew 26.

And because of His struggles and human life Jesus really understand us. He knows what it is like to suffer emotionally and physically. He understands our struggles. And He sits right there at the right hand of God intervening in our behalf.

When you really, really think about it. Not on an intellectual level but on a human level how can you not be emotional. How can you not be absolutely awed that the God of the universe loves you that much!

ChemBob
The Romans crucified lots of people, not just Jesus (assuming he is an actual historical figure), so I think human minds have been wrapped around that level of suffering. I’m sorry Kim, but I just think that all of that is nonsense and relying on writings that occurred at least 60 years after the fact is too dependent on hearsay and the frailty of human memory.

An omniscient God would already know the sorts of agony that we could experience, after all didn’t he create us? What kind of God would put any of us through the agony of Roman crucifixion or the tortures of Vlad the Impaler, or the butchery of the Crusades and wars without end?

I am emotional, I think the universe is awe-inspiring, I just don’t think there is some supernatural being that gives a care one way or the other. It’s all natural and in that context it all makes complete sense.

—At this point I told them I had work to do and needed to get back to it—

Phil
Kim I think you would agree — That Mr Powell probably went to church but unfortunately did not have a one to one, personal relationship with Christ. It is hard to deny something doesn’t exsist when you know it did. I know I have parents, siblings, cousins — how can I deny that they exsisted. While my relationship with GOD is not in the flesh, so to speak, his presence in my life and the lives of those arlound me is undeniable.

Kim
Okay…one more smat alecky comment. All the people that have ever been crucified are dead…so I don’t think any living people can actually appreciate the torture of the cross. I know people do it for a little while to experience the pain. But unless they go through the flogging (which killed most people by the way) and carrying their cross after being beaten within an inch of their lives, I don’t think they can get it.

And regarding the butchery that men do to each other. That’s not God. That’s us living in a world of sin. We do it to each other. We have free will, remember?

Okay I really have to go clean my house or the filth is going to take over. Have a good day.

Phil
Well said Kim — Praise the LORD and have a blessed holiday season. For we still serve a risen King.

Example 2: Big Z Corporation Litigation

This EDA addressed a lawsuit where a company was being sued for air and soil contamination. Although a real circumstance, the specifics of this example have been modified to maintain client anonymity.

Big Z Corporation (“Big Z”), was being sued by a regional authority (“RA”), and accused of contaminating soils in the vicinity of Big Z with zinc (“Zn”) via air deposition over a period of years. To press the suit, the RA collected soil samples from a large number of locations outside the boundaries of Big Z and had them analyzed for Zn and certain other metals. Because Big Z was assumed to be the largest user of Zn in the area, the finding of high Zn concentrations in many of the soil samples was sufficient for the RA to blame Big Z and incorporate this claim into the lawsuit, using the soil samples as evidence.

As the soil chemistry expert for Big Z and their attorneys, I performed EDA on soil concentration data obtained from the RA via the lawsuit to determine whether there was information in these data that would either indicate that Big Z was, or was not, the likely source for the metal contamination. The analyses presented in this paper are a subset of the EDA that was done, but were the central components.

Approach

The soil data were subjected to a variety of comparative, statistical and geographical information system (“GIS”) mapping procedures. This was done to assess whether sufficient information was contained in the analyses and concentration distributions to point to Big Z as the source or whether other sources might be responsible. Other sources could include natural soil mineralogy, historic activities, or other facilities. Initial procedures included comparison of the soil metal concentrations to Michigan’s regulatory requirements, the Part 201 Residential and Commercial 1 Generic Cleanup Criteria and Screening Levels, and to those concentrations found by the Michigan Background Soil Survey 2005.

Statistical evaluation procedures for the soil samples involved a range of analyses including simple descriptive statistics of individual analyte concentrations (mean, median, range, skewness, etc.) along with spatial, trend and regression analyses in an attempt to determine whether any information could be gleaned from these soil data that might indicate the metal sources. Tabulations of the metal analyte concentration data, comparisons of the concentrations to Part 201 criteria and background soil concentrations, as well as simple descriptive statistics and GIS maps depicting the concentration distributions of these metals across the sampled areas (with concentrations color-coded relative to Part 201 criteria), were compiled.

To develop a context for the EDA results, it was important to look at the industrial and urban uses of Zn, because the location of Big Z and the surrounding soils is in an area that has been highly industrialized for more than a century. In that regard, the specific industrial and commercial activities that occurred within and surrounding that area were also investigated, providing additional context for the soils data and the results of the EDA. Investigations such as these are useful and important because the EDA results can be supplemented and supported by them. The results of these preliminary investigations showed that Zn had been widely used and disseminated for more than a century in the area and that the natural soil background in the area had higher than average Zn concentrations, as did a lot of the industrial fill materials that had been used to level numerous properties across the region.

Sample concentration and location data were input into either a Microsoft Excel spreadsheet or the EDA program Aabel (Gigawiz.com) and transposed, coded or reorganized when necessary for GIS, data plotting or statistical analyses. Certain GIS displays of the concentration versus location data were done in the program ArcView 9.0 (not displayed here). Most of the statistical data analyses and visualizations were done in Aabel, although certain of the data were also evaluated using the R statistical program and some confirmatory statistics were carried out using the DataDesk program.

GIS displays did not sufficiently clarify the distribution of the Zn in the sampled areas relative to Big Z. To determine whether patterns of soil deposition might emerge in relation to potential sources using other approaches, transects were developed along lines of samples to establish whether there was any directionality to the distribution of the metals. Zn was chosen because this particular metal would be the one most expected to point to Big Z if this facility was the sole or even a primary source of Zn in the soils.

In terms of evaluating the transects analysis it was hypothesized:

1. A positive likelihood of Big Z being the source could be shown by a general increase in Zn concentrations as transects of soil samples directionally approached the Big Z property boundary (i.e., consistently increasing soil concentrations with approach to the facility); this result would require a plurality of transects showing this pattern.
2. A negative likelihood of Big Z being the source could be shown by decreases in, or randomness of, Zn concentrations as transects of soil samples directionally approached the Big Z property boundary (i.e., decreasing or random soil concentrations with approach to the facility).
3. A pattern of high soil concentrations at a generally consistent distance along the transects from the Big Z property boundaries might imply Zn sourcing by the Big Z facility (maximum soil deposition at a fairly consistent distance from the facility).
4. A random relationship in soil Zn concentrations, with respect to Big Z boundaries, increases the likelihood of numerous, possibly localized, sources for the Zn.

Following the transect analyses, an analysis of spatial concentration “zones” with distance from the Big Z property boundaries was done; the hypotheses for this analysis were essentially the same as for the transect analyses.

Results

Figure 6 provides an overview for the transect analyses, showing all Zn data in the vicinity of Big Z. This plot, created in the program Aabel, depicts:

• Big Z property boundaries (light blue area)
• Soil sample locations (all westerly of Big Z)
• Transects across the soil locations
• The general location of the Big Z operations
• Other potential stationary industrial emission sources (by facility code)
• Color-filled contours for the Zn concentrations to show areas of higher Zn concentration versus lower.

Figure 6. Sampling Locations, Transect Overview, and Zn Concentrations

The transect overview in Figure 6 displays information about sampling points that were high statistical lognormal outliers at the 95% upper confidence level for one or multiple metals (i.e., they were outside the expected concentration range based on the complete body of data at 95% confidence), which could potentially be called “hotspots,” highlighted by the type of marker:

• A small yellow-tan circle indicates that no constituents were outliers relative to the body of the data at that sampling location.
• A circular red “beach-ball” marker indicates a sampling location was an outlier for a metal other than Zn.
• A hexagonal black marker indicates the sample location was an outlier only for Zn.
• A hexagonal red marker indicates that the sample location was an outlier for Zn and at least one of the other metals.

The transects in Figure 6 are shown as orange lines or polygons. The distal end (furthest away relative to the Big Z facility) of the transect line/polygon is labeled with a capital letter and a prime symbol ( ‘ ) and the proximal end (closest to Big Z) with just a plain capital letter; for example “A’ – A” signifies a transect. A total of nine transects were done on each dataset and labeled A’ – A through I’ – I. Each transect was subjected to regression analysis, i.e., Zn concentration versus distance from the Big Z property boundary as well as individual visualization using either bubble charts or spatial bar charts. An example is provided in Figure 7 for transect A’ – A.

Figure 7. A' - A Zinc Concentration Transect

Reviewing all the figures, such as Figure 7, which displayed linear regressions of the concentration data transects showed that the r² values were quite low, indicating that linear distance/direction along the transect line towards Big Z does not account well for the concentrations of Zn. It is also observed that the slopes of the regression lines are generally not very large in either direction while scatter of the points about those lines is relatively large, supporting the r² determinations and the general lack of trending in concentration versus distance from the Big Z property.

Table 2 summarizes the r² and slope values for each transect and states whether the slopes showed increasing Zn trends toward or away from the property. The table shows that there were similar numbers of transects with slopes in either direction. This tends to support the rather dispersed random-appearing hotspots and colored contours of Figure 6. These results also support Hypotheses 2 and 4 (above), or randomness of Zn concentrations relative to the Big Z property boundaries and the likelihood of numerous localized Zn sources.

Table 2. Transect Regression Data for Zinc

As an additional check on the spatial relationship of the Zn concentrations in the soil samples relative to the Big Z boundaries, the Zn data were separated into five spatially equivalent zones corresponding to surface areas replicated using the approximate shape of the Big Z boundary, extended westward across the soil sampling space. These zones were numbered one through five and are depicted in Figure 8.

Figure 8. Spatial Zones for Zinc Analysis

All the soil samples within a zone were selected as a group and the Zn concentrations within each zone’s group were compared to all the other zones, and the total of all the zones, using statistical methods. The comparisons consisted of one-way analysis of variance (ANOVA) combined with diamond plots, to compare the means, and box and whiskers plots to compare the medians. The ANOVA and mean tables along with these plots are provided in Figure 9.

Figure 9. Comparisons of Zone Means and Medians for Zinc

The diamonds in the “diamond means comparison plots” (left, Figure 9) show the concentration means (central line in each diamond) and 95% confidence intervals of those means (tips of the diamonds) for each zone relative to the grand mean of all zones combined (dashed line across the entire plot) and to one another. When the diamonds overlap, at all, the means for each zone cannot be considered different with 95% confidence. However, when they don’t overlap, the means can be considered different. In this case, the means for all five zones are almost exactly equal to one another, and to the grand mean, and the diamonds fully overlap.

The same holds true for the medians (straight connected lines) in the box and whiskers plot of Figure 9 (right plot), with very small differences in their values and overlap of the 95% confidence “notches” surrounding the medians. The overlap of the notches indicates that the medians cannot be considered different with 95% confidence. These statistical plots indicate no significant differences in the means or the medians of the five zones as one progresses westward away from the Big Z property boundary. The plots are fully supported by both the mean and ANOVA tables in Figure 9. The means, standard deviations, and both lower and upper 95% confidence intervals are almost identical in the means table while the ANOVA table yields a probability (column “P > F”) that indicates none of the means has a significantly different value from the others. The average amount of Zn in the soils is the same, whatever the distance from Big Z.

To further eliminate any doubts or questions about the equivalency of the Zn means and overall zone concentrations, two additional modifications were made to the data for further comparison (figures not shown): (1) The natural log (ln) values of the Zn concentrations within each zone were similarly evaluated and there were still no significant differences between the means and medians. (2) Likewise, all potential statistical Zn high outliers were removed from the evaluations with the same result; no significant differences between means or medians.

These concentration zone results, particularly when combined with the transect analyses, show that there is no spatial relationship of Zn soil concentrations to Big Z whatsoever and make it virtually impossible that the source is solely Big Z activities. It would be difficult or impossible to envision a scenario in which the mean soil concentrations of Zn would be exactly the same at any zonal distance from a facility in an urban area if that facility was to blame for the concentrations of the Zn in those soils. The results are far more indicative of long-term widespread deposition from numerous sources, combined with high native Zn topsoil content and with many instances of localized inputs (hotspots) that account for the high Zn values that were found in a few of the samples.

In summary, the EDA of the Zn soil data seem to indicate that Big Z was not predominantly responsible for the Zn present in the soils within the area.

SUMMARY AND CONCLUSIONS

Using the techniques of exploratory data analysis, particularly with current software packages, can be a rapid and effective way to formulate and test hypotheses using real-world data to achieve the goals and requirements of understanding site scenarios. It allows insight into the relationships among the data components through visualization of the data and elucidation of their patterns, trends and associated statistics. EDA allows the development of conceptual site models that are reality-based and are generally easy for clients and regulators to understand. It is particularly helpful for complex sites or when trying to answer complex questions about a site.

I have presented two examples of how EDA was used to resolve specific and important environmental questions. In the first example, it was possible to develop a general understanding of the locations, structure and relationships among the contaminants in Manistee Lake sediments in Michigan. EDA was then used to assess whether single contaminants or suites of contaminants were responsible for the toxic effects of the sediments on benthic organisms and then identified the most likely suite and source from all the possible contaminant combinations. Based on these results an “Action Plan” for the stakeholders and a further refined plan for sampling Manistee Lake were developed.

In the second example EDA was used, in conjunction with an historic and current understanding of the sampled area, to show in the context of a litigation that it was not possible for a company to have been the sole or primary cause of metals contamination in residential soils west of the facility.

REFERENCES

Note: These references are for Parts 1 and 2 of this series also.

1. “Part 201 criteria.” Part 213 Tier 1 Risk-Based Screening Levels, of the Administrative Rules for Part 201, Environmental Remediation, Michigan Public Act 451, Natural Resources and Environmental Protection Act, of 1994, as amended.
2. J.W.Tukey, “Exploratory Data Analysis”, 1977, Addisson Wesley.
3. DataDesk (http://www.datadescription.com/)
4. Aabel (http://www.gigawiz.com/)
5. Schaetzl, R. J., 2004. GEO 333, Geography of Michigan and the Great Lakes Region. http://www.geo.msu.edu/geo333/MIwatershed.html.
6. Kazmierski, J., Kram M., Mills, E., Phemister, D., Reo, N., Riggs, C., and R. Tefertiller. “Upper Manistee River Watershed Conservation Plan.” Prepared for The Grand Traverse Regional Land Conservancy. M. S. Project. Donna Erickson, Faculty Advisor. University Of Michigan. School of Natural Resources & Environment. April 2002.
7. Rediske, R.; Gabrosek, J.; Thompson, C.; Bertin; Blunt, J.; and P.G. Meier. Preliminary Investigation of The Extent of Sediment Contamination in Manistee Lake. AWRI Publication # TM-2001-7, Great Lakes National Program Office #985906-01, July 2001.
8. Velleman, P. F. (1997). DataDesk Version 6.0, Handbook, Volumes 2 and 3. Ithaca, N.Y., Data Description, Inc.
9. Michigan Background Soil Survey 2005. Hazardous Waste Technical Support Unit, Hazardous Waste Section, Waste and Hazardous Materials Division.
10. ArcView 9.0 (http://www.esri.com/)
11. R (http://www.r-project.org/)

KEYWORDS

Exploratory Data Analysis, evaluation, analysis, environmental, statistics, hypotheses, data, inference, litigation, toxicity, testing, comparisons.

Charles Darwin on my bookshelf

Darwin’s 150th anniversary of “On the Origin of Species,” also his 200th birthday, has been a busy year, both for evidence that further cements evolution as fact and many books celebrating evolutionary theory as the most amazing realization that mankind has ever experienced.

And this is what evolution truly is; it is far more magnificent than the poetry and allegory of the book of Genesis, because it is real. It is what has surrounded us all our short lives, but on a time scale that makes it difficult to observe. Once you think about the mechanisms of evolution and understand the processes even slightly, it becomes obvious. This is why evolution makes the creationists and intelligent design “cdesign proponentsists,” folks crazy(ier). Evolution is obvious, it is explanatory, it is predictive, it is convincing in a way that 5000+ years of religious creation myths, try though they have, have not been able to achieve.

Religion requires blind faith, while evolution paints a picture with the oils of reality that is unrivaled by Michelangelo’s finest brushstrokes above the Sistine Chapel. Evolution is logic supported by evidence, truly the greatest show on earth, a genetic masterpiece rendered in biology and chemistry, no faith required, and it syncs perfectly with what we all recognize as reality once our blinders are removed.

If you’re reading or listening to any recent books or articles on evolution, fossil finds, or Darwin, etc., please mention them in the comments below.

That’s a frightening page, but people tend to forget something. Money is a totally made-up, human-constructed concept, developed only to serve as a representation of goods and services and eliminate the need for everyone to constantly haul goods and services around. However, this increasingly abstracted representation, which is no longer limited to actual currency but includes contracts, credit cards, loans, 401Ks, mortgage bond derivatives, etc., has made it far easier to blur the line between what are useful goods and services versus unnecessary junk and services having no intrinsic value.

There is a rationality to thermodynamics and the flow of materials and energy that has not yet manifested fully in our society because advances in the technology of energetics transfer, i.e., increasing efficiency, have delayed its inception. In the meantime, this efficiency increase has resulted in maintaining a hyper-populated planet, with enough residual to mask the gargantuan waste of natural resources . This, in turn, has made it very easy for persons in certain professions to game the system and steal lots of the money that is, quite simply, the representation of our energetics in a societal model. In a very real sense, these people are stealing your energy, your outputs and, as they do so, are modifying the social structures to make it even easier for them to continue doing so.

Now, maybe in some circumstances that is not such a bad thing. Clearly it is advantageous to society (us) as a whole to take the best ideas, the best inventions, the best innovations, and market them in a manner that allows everyone to benefit from the good that they do, from the illumination that they cast on our understanding, from the artistry they afford, from the comforts that they provide our physical selves. However, given enough time and enough motivation, the honest brokers of these interactions will be sufficiently replaced by the “cheaters” described so well by sociobiology, to the extent that a large portion of our efforts will be diverted into enriching these few, with less of the positive “trickling down” to the rest of us. Given enough experimentation, the cheaters can determine, with a very narrow range of error, just how much we can tolerate with regard to the efforts we expend versus the gains we achieve from that expenditure. Then they walk that line and they acquire our energy ($$) while we consume and learn to be unhappy if we cannot consume enough. But the leash is short, we are mollified, and we almost always choose the status quo if we believe, “things could be worse.” And if we become measurably unhappy because we cannot attain all of the consumptive goals, we are told to compare ourselves to false Libertarian ideals, such us the cartoonish caricatures of Ayn Rand, as being the heroes to which we should aspire. We are being gamed, far beyond our necessity, far beyond what is required for life, liberty, and the pursuit of happiness.

Maybe it’s time for a different approach to exchange of goods and services. The fact is, a global society cannot go on with the same old approach to economics and consumption, where a company must be trying to constantly grow to increase it’s profits, hence it’s market capitalization, hence its enrichment of a few executives. There is a limit to a global approach wherein countries are constantly going to war to sequester resources for their corporations. We are operating in a closed system with regard to major natural resources, and the science of physics is absolutely clear on what that means.

There cannot be constant growth in business, nature simply doesn’t allow for constant, unchecked growth in anything. We either have to adapt to current reality, to the current ecology of our surroundings, or we become unsustainable and we crash, including massive declines in populations. We have to find an equilibrium approach with sustainable methods of survival and happiness that don’t always depend upon having more “stuff” or more money than everybody else; one that prevents robber barons from plying their nefarious trades in the shadows. This is true on all levels, government, business, and personal.

Perhaps looking to nature, in particular our current understanding of biology, ecology, ethology, physics, and chemistry could help us brainstorm and initiate experiments with new approaches to bartering and trading for our energetics. Ideas and criticisms to move thinking forward in this area, or to point out why I’m off base, are very welcome in the comments below. Let us not go gentle into that good night. We CAN fix this.

Do not go gentle into that good night,
Old age should burn and rave at close of day;
Rage, rage against the dying of the light.

Though wise men at their end know dark is right,
Because their words had forked no lightning they
Do not go gentle into that good night.

Good men, the last wave by, crying how bright
Their frail deeds might have danced in a green bay,
Rage, rage against the dying of the light.

Wild men who caught and sang the sun in flight,
And learn, too late, they grieved it on its way,
Do not go gentle into that good night.

Grave men, near death, who see with blinding sight
Blind eyes could blaze like meteors and be gay,
Rage, rage against the dying of the light.

And you, my father, there on the sad height,
Curse, bless me now with your fierce tears, I pray.
Do not go gentle into that good night.
Rage, rage against the dying of the light.

– Dylan Thomas

This example addresses how EDA was used on data from Manistee Lake, Michigan. The work was funded by the Little River Band of Ottawa Indians (LRBOI) through a grant from the U. S. EPA. Any references cited will be included in Part 3.

Part 3 of this series should be coming soon. It will focus on a lawsuit against a major manufacturing facility and how, as an expert witness, I used EDA to show that the accused manufacturer could not be held liable for metal contamination of neighborhood soils.

Example 1: Manistee Lake Sediment Toxicity EDA

The overall goals of this project were to:

• Review available information on the sediments in Manistee Lake. Evaluate these data using statistical exploratory data analysis (“EDA”) to develop a conceptual site model (“CSM”) based on available data to help understand the contaminant issues in the lake.
• Develop an “inventory and action plan” that assessed the major sources of industrial pollution in Manistee Lake, the major sediment pollutants of concern, and proposed courses of action.
• Create a presentation to increase local Manistee Lake awareness and begin discussions.
• Evaluate and design a plan for further sampling of the lake.

A portion of the EDA used to accomplish the first of these goals is presented in this blog posting.

Manistee Lake Setting and Historical Impacts

Manistee Lake is in the Manistee watershed, which encompasses more than 5000 square kilometers, 1930 square miles or 1,240,000 acres. The lake is a drowned river mouth, fed by the Manistee River from the northeast and the Little Manistee River from the southeast. A channel connects the lake to Lake Michigan. Flow is generally SE to NW with crossflow across the northern portion from the Manistee River west to the Lake Michigan channel. Manistee Lake itself has an area of about 930 square acres and a maximum depth of about 50 feet. Manistee Lake was once a large bay of Lake Michigan; water levels dropped and they became separated by sand bars and low dunes.

The history of Manistee Lake includes more than a century and a half of industrial usage. Contamination of the lake bottom sediments is extensive and profound. This has resulted in the near elimination of the natural populations of sediment-dwelling benthic organisms, creating a negative impact on the lake at all trophic levels.

Study Background

This EDA sought to better understand the nature and impacts of some of the sediment contaminants that were quantified in Manistee Lake at 14 sampling locations (Figure 1) in a study by Rediske et al. (2001) and to discern whether additional insights could be gained from the data accumulated in that report for better understanding of the lake’s environment.

Figure 1. Manistee Sampling Locations from Rediske et al.

Approach

EDA focused initially on the metal and metalloid contaminants, total organic carbon (TOC), and hexane extractable materials with regard to descriptive statistics, depths in the sediments, concentrations, and sampling locations. Further evaluation added additional contaminants, their concentrations and locations, and potential pollutant sources. Sediment organism mortality percentages and counts were evaluated within this context, with a particular emphasis on trying to identify correlations between organism mortality, individual or multiple contaminants, and industrial activities that might be the source of the contaminants.

A detailed evaluation of the chemicals versus four organism studies using stepwise multiple regression analysis was conducted and the most likely chemical causes of organism mortality were identified. Following an inventory of industrial activity in the vicinity of the lake and an assessment that scored these activities for the “significant contamination factors,” (i.e., the contaminants from the stepwise regression analysis that most impacted the organisms) these contaminants and organism mortality results were plotted versus location along the length of the lake with the highest scoring industrial activities highlighted. A conceptual site model for sediment organism mortality was the result.

Results

The first analysis was to determine whether any of the constituents determined in the sediments were found preferentially at any particular depth(s) in the sediments. This was done by creating both dotplots and box and whisker plots of each of the constituents versus depth level. Figure 2 displays, as an example, such a box and whiskers plot for mercury (modified by assigning the reporting limit to samples reported at less than that limit, i.e., a conservative approach). The depth level S is for surface samples acquired using a Ponar sediment sampler whereas T, M, and B refer to top, middle and bottom core sections, respectively.

Boxplots have four components; more rigorous definitions of which can be found in Velleman. These are:

• The outlined central box depicts the middle half of the data between the 25th and the 75th percentiles.
• The horizontal line across the box marks the median.
• The whiskers extend from the top and bottom of the box to depict the extent of the main body of the data.
• In addition, extreme data values are plotted individually, usually with a circle.
• Very extreme values are plotted with a starburst.

Figure 2. Box and Whiskers Plot for Mercury

In addition to these components the 95% confidence intervals of the data can be depicted with a shaded area. If the shaded areas for two or more groups do not overlap then there is 95% statistical confidence that their medians are different. Using such plots it was possible to observe that, in general, the contaminants of concern were primarily found in the uppermost lake sediments (Figure 2, S and T), potentially indicating anthropogenic origin.

Linear regression of the contaminant concentrations versus sample locations in the flow direction along the length of the lake (not shown) were then done. These plots showed that many of the contaminants, particularly in the shallow samples, increased in concentration from the S end to the N end of the lake, along the flowpath. This indicated that the flowing water might be transporting contaminants in a cumulative manner to the sediments as it passed industrial locations along the shoreline.

Rediske et al. performed two types of biological studies. One of these consisted of counting organisms in the collected sediment samples at each location. A value was assigned to the total number of organisms counted for each location and the species were noted and counted. This resulted in two biological values for each surface sampling location that were used in this EDA:

1. Organisms (total), and

2. Species #

The second type of biological study was independent laboratory tests of acute toxicity, via exposure to the actual sediments collected at each location, for two types of organisms. The organisms were:

1. Hyalella Azteca (amphipod), and

2. Chironomus Tentans (midge)

Eight replicate toxicity tests were done with each organism for each location. For the EDA the final average number surviving at each location was converted to a percent mortality value.

Boxplots of the organism studies indicated that the sediment control location mortalities (Figure 1, M-1 and M-14) were very different from the others along the length of the lake. By the time one arrives at Locations M-2 and M-13, organism mortality/disappearance is worsened by a range of factors from 1.6 to 5.5. This indicates significant sediment toxicity in the industrialized areas of the lake.

Area charts of the four sets of biological study results are displayed versus sampling location in Figure 3.

Figure 3. Organism Area Plots vs. Sampling Location

The charts for sediment mortality to H. Azteca and C. Tentans are stacked (Figure 3, top), whereas the magnitudes of the values for Species # and Organisms (Total) were too different for stacking without applying a data transformation. The percent mortality of the two organisms track one another very well and appear to be almost perfect counterparts to the organism and species # counts, with increased percent mortality corresponding very closely with reductions in the numbers and species of organisms natively present in the sediments. These plots illustrate the extreme toxicity of the shallow Manistee Lake sediments just beyond the river mouths (i.e., the control locations) within the lake.

Accounting for the specific environmental factors that contribute to the sudden increase in organism mortality upon entering the lake environment is important for a variety of reasons, including:

1. The levels of these toxic chemicals need to be reduced to increase biodiversity
2. Identifying the primary contaminants of concern might also identify the sources, creating an opportunity to control their discharge
3. Predictions of residence time and fate in the sediments might be possible based on the contaminant chemistry and the geochemistry
4. Additional sampling with increased focus on the most toxic areas can be planned
5. A conceptual model of the lake bed and its environment can begin to be developed.

The next part of the EDA plotted the results of these organism studies versus their position along the lake. Figure 4 illustrates the complexity of understanding the specifics of toxicity to these organisms by also depicting stacked area charts of a variety of selected contaminant concentrations.

Virtually every measured contaminant in these surface sediment (Ponar) samples increased dramatically in concentration beyond the river mouth locations (control Locations 1 and 14) within the lake (Locations 2 through 13). The Percent Mortality lines for H. Azteca and C. Tentans overlying these area graphs (Figure 4) show the extremely close correspondence between the most highly contaminated zones and the toxicity of the sediments to these organisms. Cumulative, rather than individual, toxic contaminant impacts might be implied by these charts, so that possibility was further evaluated.

To determine whether individual toxicities or combined contaminant impacts were responsible a stepwise multiple regression analysis was the next phase of this EDA. This was done using the program DataDesk to develop a “model” for the impacts of the various contaminants on the organisms. The familiar simple linear regression, y = mx+a, describes the relationship between a response variable such as mortality (y) and a predictor variable such as a contaminant concentration (x). The data are plotted as a scatterplot that shows the datapoints, a regression line (based on the regression equation where m = slope and a = y axis intercept), and confidence interval boundaries, usually set at 95% confidence for environmental studies. A multiple regression includes more than one predictor variable to try to further account for the response variable values. However, the results of a multiple regression become difficult to visualize. Using only two predictor variables the straight line of the simple regression becomes a flat surface. Further addition of predictors adds additional dimensions. Because of this, numerical values must be used to explain the model rather than figures.

To understand whether predictor variables (such as arsenic, lead, hexane extractables, etc.) are significant and predict response values (such as H. Azteca % Mortality, Species #, etc.) requires the interpretation of tables containing t-ratios, probabilities, and R² fit values. A discussion of these values and their interpretation is beyond the scope of this blog but can be found in standard statistics textbooks and the references cited. In summary, the Pearson Product Moment Correlation values for the selected organism study, versus all the possible predictors, (contaminants) is developed. This yields the residual correlations used in the stepwise regression analysis. The predictor variables are then individually added stepwise (hence the name) to the regression model, each causing its own residual correlation to go to zero and generating a probability. Regression begins with the highest residual correlation. In the case of H. Azteca this was arsenic at 0.756 residual. Additional contaminant predictors were added to the regression until the t-ratio probability of the last added predictor was greater than 0.05, indicating that that contaminant was not significant to mortality of the organism at the 95% confidence level.

The results of the stepwise multiple regression analyses are provided in Table 1:

Table 1. Summary of Regression Model Results for the Biological Studies

The stepwise multiple regression analyses indicated that certain sediment contaminants were well correlated with the loss of organisms in Manistee Lake. These contaminants and their apparent order of importance were:

As > Cr, hexane extractables, PAH > Hg, Se

Following this modeling, facilities along the lake shoreline were “scored” with respect to industrial processes and on-site materials that might generate this particular suite of contaminants. The highest scores were associated with facilities that stored coal or coke in piles along the shoreline of the lake. Figure 5 depicts the locations of these coal storage piles versus these important sediment contaminants from the stepwise regression and plots these versus the organism mortality values.

Figure 5. Coal Pile Locations Relative to Correlated Contaminants and Mortality

The results indicate that coal contamination of the bottom sediments along the shoreline seems to exert a substantial negative impact on the benthic organisms in those areas and immediately downgradient from them.

Conclusions

Several important conclusions resulted from the EDA study of Manistee Lake. These were:

• The contaminants of concern were primarily found in the uppermost lake sediments,  indicative of anthropogenic origin.
• Many contaminants increased in concentration along the flowpath.
• Area plots of the results of the sediment organism studies versus sample locations showed very good correspondence among the four biological approaches.
• Mortality/disappearance worsened by factors of 1.6 to 5.5 immediately within the lake relative to controls.
• There was excellent correspondence between organism mortality and locations of very high total contamination concentrations.
• Stepwise multiple regression analyses indicated that certain sediment contaminants were well correlated with the dearth of organisms in Lake Manistee.

EDA elucidated that facilities hosting coal/coke storage piles were most strongly associated with contaminant concentrations and organism mortality, establishing a major portion of a conceptual site model. The results illustrate where cleanup could be focused to increase the populations of benthic organisms in Manistee Lake and increase its biodiversity.

Upcoming

Stay tuned for Part 3, which should conclude this series on EDA.

ABSTRACT

In the process of doing environmental science, large amounts of data are usually collected at significant expense. Often these data are used in specific but fairly superficial ways; for example, to determine whether regulatory criteria are exceeded or as “evidence” that property or health has suffered damage. Any remaining information in the data is often not extracted.

However, there are difficult environmental questions and problems that require a more comprehensive understanding. If you are called upon to determine why there is high organism mortality in a lake, to ascertain whether a client is responsible for contaminating a neighborhood, or to assess whether the concentrations of metals in soil are due to facility operations, natural background levels, or historic fill, tables of analytical data and t-tests are usually neither sufficient nor convincing. Often trends and patterns in environmental data do not readily emerge from tables and numerical statistics, but understanding this hidden information can be crucial for answering such difficult questions and solving recalcitrant problems.

Exploratory and inferential data analysis (“EDA”) provides rapid and comprehensive approaches for finding patterns and trends in data using statistical methods. Good EDA programs provide rapid visualization of statistical and geostatistical data across multiple variables and allow efficient hypothesis testing to determine whether the trends and patterns are statistically significant. In fact, preformed hypotheses are not necessarily required because hypotheses can emerge from the trends and patterns. In combination with appropriate educational training and professional experience, which are needed to understand and explain the emergent patterns, EDA is a powerful tool for answering environmental questions and finding solutions to environmental problems. The posts to follow will describe and illustrate how EDA was used to process data from actual sites and answer the questions posed above.

INTRODUCTION

Large quantities of data are typically generated when we assess the natural state of the environment or its responses to our anthropogenic activities and, quite often, a large portion of the informational content contained in the data is not accessed. These collected environmental data are generally used in important ways, such as to assess risks to human health and other organisms, to establish baselines to which environmental changes can be contrasted, as indicators of trends in such environmental changes, as criteria for the onset of regulatory actions at a given concentration based upon studies of risk, as the basis of lawsuits for damages believed due to these environmental conditions, and so on.

Quite often, the scientists and engineers using the data are looking for something very specific from the data, such as to negate concerns that the soils and groundwater exceed specific regulatory criteria, such as the Michigan Part 201 criteria (“Part 201”). At a more complex level of need, the data user might be trying to make decisions about whether active remediation is needed, whether monitored natural attenuation is occurring at an acceptable rate, to determine whether enough data have been collected to minimize potential data gaps, or to make an assessment of how and where to install active systems for remediation of contaminants. I have required relatively complex data queries to, among other tasks:

1. Review available information on the sediments in Manistee Lake and determine why the sediments in the lake are essentially devoid of benthic organisms in certain areas,
2. Resolve whether a facility is responsible for widespread contamination of an area when the facility is being sued for that contamination, and
3. Spatially and volumetrically discriminate between natural soil constituents, industrial fill materials and waste-contaminated soils at old industrial sites.

Exploratory and inferential data analysis can often achieve the goals and requirements of understanding complex scenarios by allowing the collected data to be manipulated in a variety of ways to achieve quick testing of hypotheses and yield insight into potential alternative hypotheses through visualization of the data. EDA can provide rapid and comprehensive approaches for finding patterns and trends in data using spatial and statistical methods. In the process of developing informative graphical displays of the data, EDA can generate numerical values for the appropriate statistics to indicate whether or not the relationships among the different datasets are significant. Conceptual site models (“CSM”) can be developed using EDA and these are generally much easier for clients and regulators to understand than simple tables or maps of sample locations that contain lists of contaminant concentrations. This is particularly true for complex sites or when asking complex questions about a site.

The body of this post consists primarily of describing subsets of the EDA done on two real-world examples, numbers one and two above, to illustrate how an EDA approach can help scientists understand and resolve difficult or seemingly intractable issues.

EXPLORATORY DATA ANALYSIS

J.W. Tukey coined the term “exploratory data analysis,” in 1977. He defined and described EDA as follows:

“…the examination of data with minimal preconceptions about its structure through which it is hoped that relationships and patterns, at least some of which are unanticipated, will be uncovered.”

“The principal theme is flexibility of technique. Good exploration requires many and varied analyses of the same data, and a certain amount of trial and error is expected. Creativity in approach may, therefore, be crucial. Precision and efficiency in any particular analysis are not nearly as critical as robustness and convenience.”

“A secondary theme is that structures and patterns in data fall into two broad classes: the obvious and the surprising. The techniques discussed can be correspondingly divided into those designed to display clearly and simply the first level of structure and those intended to look beyond those relations to uncover the unexpected features underlying what confirmatory analysis might dismiss as residuals or errors.”

When Tukey developed EDA virtually all aspects of the exploration had to be calculated manually. In the interim, many computer programs have been developed that leverage his ideas and allow the rapid exploration and analysis of data. The majority of the data explorations and statistical charts, tables, other displays and values presented in this manuscript have been generated using either the program DataDesk or Aabel, which used Tukey’s approach to examining the data to the extent possible.

OK. That’s it for Part 1. The first example (Part 2) will be about an EDA investigation that sought to better understand the nature and biological impacts of sediment contaminants that had been found in Manistee Lake, Michigan. Part 3 will focus on the use of EDA in a lawsuit where a large company was being sued in federal court for air and soil contamination and I was the expert witness for the defendant.

There are numerous life circumstances that can influence scientific judgment. Not least of these is the circumstance of employment and financial security.

Government and university-based research is generally considered to be relatively unbiased compared to private sector science. Jobs are often secure, salaries are generally fairly standardized and decent. However, most scientists are now employed in the private sector and much research is done there as well. In the private sector jobs are often only as secure as the profit margin is robust. Having a robust profit margin often depends upon selling the product being researched or by keeping the clients happy. The robust profit margin, in turn, usually results in a robust salary for the scientist.

Private sector science can create ethical concerns and, if ethics are compromised by results that are biased to suit corporate needs, can cause scientific confusion. This confusion can become manifest in the scientific literature, in the media, in the public understanding of science and, I would argue, in the minds of the corporate scientists themselves.

If a scientist is under employment duress, imaginary, unstated, or otherwise, it can alter his/her frame of reference with regard to hypothesis testing, data analysis, and contextualizing results. The results don’t have to be deliberately wrong or misrepresented, they can just be incomplete, mis-emphasized, or only partially analyzed or discussed. I suspect that, in many cases, the scientist is not even conscious of the oversight but confused, having subconsciously confounded and rationalized at least three sets of needs, which are not necessarily compatible:

1. Personal and familial security
2. Corporate goals and approval
3. Proper scientific truth-seeking methodology

These oversights and confusion are compounded by the time and financial constraints under which the corporate scientist is usually working. The tension between billable hours and limited research budgets (relative to other direct project costs) add an additional layer of complexity to already difficult work. Shortcuts are often sought that attain minimally adequate results.
When poor scientific work proves to be a success for the company, the sorts of errors embedded in that methodology can then be carried on into the next project, possibly even magnified a little. Then it can become habitual and iterative, portions basically becoming boilerplate approaches and interpretive styles. Hopefully the scientist will, at some point, develop sufficient cognitive dissonance to realize that he/she/the company has a problem and is likely to be misrepresenting reality. If not, at some point it will potentially become obvious to others that the data are insufficient to support the conclusions. At this point damage has been done to the company, its products, the clients and the scientist. It has been my observation that it is always better and more profitable, for everyone involved, to do science correctly from the outset whether the initial results are what were desired or not.

This isn’t to say that university and government scientists don’t have their own needs and desires that can addle their judgment, they do. But, realistically, much of the work done in those sectors tends to be peer-reviewed, while private sector science is not and is, in fact, often confidential. These non-reviewed, confidential results can have surprisingly significant impacts, not only on corporate clients, but on the public commons and the overall well-being of our society.

It is incumbent on all scientists to police themselves and their results to the best of their abilities, because there is a distinct possibility that no one else will be able to do it.

The November election seemed to dampen the concern briefly; after all, President Obama appointed Nobel Prize winning physicist Steven Chu to head the Department of Energy, along with a host of other top-shelf scientists throughout the government. But during the past few weeks this smoldering issue of societal ignorance has reignited like a pile of pure white phosphorous bathing in oxygen with a moist sponge atop a tire pile. OK, maybe that’s an overstatement. Anyway, this recent ‘conflagration of controversy’ followed the release of the book, “Unscientific America,” by Chris Mooney and Sheril Kirshenbaum.

This post isn’t intended to be a review of M&K’s book; I haven’t read it and probably won’t (unless I get a review copy, hint, hint). I admit however that, having read a number of comments by Mooney on various blogs, including Pharyngula and Daily Kos, and listening to him being interviewed on several podcasts, I am more likely to lean, to an acute angle, in the direction of those who are critical of the book; most of those critics being scientists.

Apparently a contentious tenet of M&K’s book is “concern trolling” about how we scientists generally do a crappy job of communicating science to the lay public. So M&K use their book to criticize scientists and give us guidance that everyone already knows. M&K particularly go off on scientists who are atheists and declarative about their atheism, apparently approving of Darwin’s lassitude about religion while belittling Richard Dawkins and actively reviling P.Z. Myers for their vigorous anti-God’ism. M&K seem to think that the nouveau atheist scientists, particularly those strongly trying to defy the religious meme, turn off and drive away the masses of potential Einsteins who believe in God. Or so I paraphrase from reviews I’ve read.

But maybe it’s not so much that scientists aren’t adequately communicating science, maybe the general public is, generally:

1. Preoccupied with survival
2. Just not interested in science
3. Busy watching sports and reality TV
4. Think science is hard
5. Think science is for nerds and they’re not nerds
6. Actually enjoy not knowing what’s real and what’s not
7. Have an uncomfortable feeling that knowing too much would challenge their beliefs
8. Just don’t believe that knowing something should require evidence and a process, their experiences and what THEY know are unquestionable
9. Have been told that everything they need to know is in the Bible.

This list isn’t nearly inclusive and the entries are individually too simplistic to describe, well…anybody. But when you start making combinations from the list…

I personally don’t think that we scientists are doing such a bad job of communicating science. There’s a lot more science on TV, for example, than has ever been previously available. There’s even a Science Channel with Michio Kaku hosting shows. The Mythbusters do empirical science and make it accessible to anyone smart enough to change the TV channel to watch it.

The internets tubes are filled with science, if you know enough science to recognize it and are interested enough to seek it. There are lots of podcasts larded with pure sciency goodness and the skeptical podcasts are always talking about the scientific method, logical fallacies, evidence-based reasoning, and so on. I’m perpetually weeks behind on my podcast listening, but that’s because there are more good ones than I have drive time. I’ve put a list of my favorites, below. Please leave a comment and list some of yours, so we can all check them out. Perhaps we can recommend some of them to non-sciency friends and relatives who might actually drink if led to the water.

ChemBob’s Podcast Playlist:
(in no particular order)

1. Skeptic’s Guide to the Universe
2. NPR’s Science Friday
3. This Week in Science
4. Diffusion Science Radio
5. Point of Inquiry
6. Nova Science NOW
7. The Naked Scientists
8. The Atheist Experience
9. Freethought Radio
10. Reasonable Doubts Podcast
11. Skepticality
12. WNYC’s Radio Lab

OK, that’s my podcast listening list. Can you suggest some we can recommend and further blunt the Mooney and Kirshenbaum criticism? Use the comments!