If you’re interested in contributing to the local skeptical movement in the greater Phoenix area, please contact me at matt@phoenixskeptics.org.

- Matt

Join us next month on March 15 for a discussion on Chris Mooney’s The Republican Brain. From Amazon.com:

Science writer Chris Mooney explores brain scans, polls, and psychology experiments to explain why conservatives today believe more wrong things; appear more likely than Democrats to oppose new ideas and less likely to change their beliefs in the face of new facts; and sometimes respond to compelling evidence by doubling down on their current beliefs.

March’s book club comes immediately after an upcoming special event: A Evening With Chris Mooney on Monday, March 11, 2013. It will be interesting and fun to discuss Mooney’s book after meeting him in person. Don’t miss either event!

(Special thanks to Rose for helping with this entry in my absence.)

Next we discussed Mendel in the Kitchen: A Scientist’s View of Genetically Modified Food by Federoff and Brown. This book attempts to tackle the issues of whether genetically modified food is safe and whether common safety concerns are valid. The book describes the history of agriculture and plant modification beginning with the so-called “natural” breeding processes to the more modern “genetic modification.”  The authors argue that genetically modified food isn’t much different from “natural” hybrid crosses farmers have been using for many years, and assets that this method is less likely to result in error. The authors also explain how the use of genetically modified food could reduce the amount of pesticides and fertilizers in the environment while simultaneously feeding a fast growing population.  Those members of the book club who were wary about genetically modified food were somewhat swayed by this book’s pervasive and well-backed arguments regarding the safety of genetically modified food and its potential for environmental conservation.

Join us on Friday, February 22 for our next Skeptic Book Club Discussion on Gender Differences. We will be reading two books with varying views on the issue: The Red Queen: Sex and the Evolution of Human Nature by Matt Ridley (2003) and Delusions of Gender: How Our Minds, Society, and Neurosexism Create Difference by Cordelia Fine (2011).  Please check out Phoenix Skeptics in the Pub at http://www.meetup.com/phoenixskeptics for more information and to RSVP.

According to CBS 5’s Scott Davis ghost photos may not be picture perfect, and even some paranormal investigators agree. In a recent article Davis wrote, “So what do ghosts look like on camera?

First of all, they’re not orbs. You’ve seen photos with white or clear ‘balls’ in them. Most serious investigators dismiss these as simply specks of dust, moisture or even insects reflecting the camera flash.”

Davis even took the time to sit down with the Phoenix Area Skeptics Society’s own president and chairman Matt Londen.

“ ‘It’s fun to believe in this stuff,’ Londen said. ‘But I would guess that almost all of what people would call ghost photos can be explained by several things like photographer error- the strap gets in the way, or dust gets in the lens or the flash, or outright hoaxing, to anything else that’s more mundane than ghosts.’ ”

Ghost photography has been around just as long as cameras. In the 1800s several photographers caused quite a stir with their pictures- many of them obvious hoaxes, using double exposures and other tricks.

Davis also interviewed a member of a local group the East Valley Paranormal Society, Kale Kelly the tech manager for the group said “I always tell people when they say, ‘what’s the most important piece of equipment you can have’ and it’s your brain!”

Davis reported “Londen says it’s hard to find anything objective to measure against. ‘Let’s say I wanted to know if the picture I have of an orb is dust. I would do an experiment where I recreate that scenario- with dust- take a picture and see if it looks the same. How do you do that with a ghost? You don’t have a real ghost to compare to!’ ”

A local news outlet recently reported about the supposed end of the world that is set to happen on December 21 of this year. According to the article many people believe this to be the date of the apocalypse.

“The Mayan calendar ends on December 21, 2012, so many people believe that date to be the end of days.

According to CNN , the calendar had a 5,126-year cycle, so many wonder why the ancient civilization, known for their advanced grasp on astronomy, chose this date.”

There are many problems with this view, but the most glaring is that according to Discovery News, the Mayans never predicted doomsday! “The end of a calendar cycle doesn’t mean it’s doomsday. Lacking a culture to renew the Long Count, it looks like the final cycle — the 13th bak’tun — will come to an end… and that’s about it.

All the hype surrounding marauding celestial bodies (Planet X, Nibiru, errant asteroids and comets), weird cosmic occurrences (killer solar flares and galactic alignments) and crazy Earth-shattering events (polar/geomagnetic shift), is just that, hype.

The Mayans, on the other hand, are real and their descendents are planning on having a big party on Dec. 21. It is a reason for celebration, a time to remember the last cycle of a wonderful calendar system that represents the last breath of an ancient civilization.”

Here in Phoenix you can celebrate the un-ending of the world with the Phoenix Area Skeptics Society! More details will be released as the non-doomsday draws even closer.

It was not the fact that Swiss mentioned skepticism that was eye opening.  I’ve been a member of Phoenix Skeptics in the Pub for a little over a year now, and have been pursuing skepticism and science for most of my life.  I’m no stranger to how skepticism can help shield us from deception and self-deception.  The thing that really opened my eyes and made me think was one statement in particular that Swiss made: why aren’t we teaching the methods, fundamentals and philosophies of science in 2nd or 3rd grade instead starting these classes in college?

Holy crap!  Why aren’t we teaching the methods, fundamentals and philosophies to kids?!  We aren’t teaching grade school and high school kids how to think.  We’re only teaching them to memorize and regurgitate facts for things like the AIMS test.  Kids today are losing their critical thinking skills, and, as a high school science teacher, I have to admit that I’m as guilty of this as any other teacher.  All of the focus  of teaching today is on test scores and knowing facts, but not how to think problems through.  Why the hell are we teaching this very fundamental skills to children?!  How can I, as a teacher, design lessons, units, curriculum, and classes that promote, teach, and explore critical thinking and the philosophies of science?

So here’s my call – I need help from other teachers!  So I’m calling out PASS teachers.  Help me bring science fundamentals and critical thinkings skills back into the classroom.  Leave a comment below if you’re interested in meeting up to plan out some lessons.

For this blog entry, I’d like to discuss Statistics and Beer. The discerning reader may be questioning this relationship but, in fact, one of the most commonly used statistical tests, Student’s t-test, was developed in the quest to make a better beer.

The Story of Student’s t-test

In 1899, a man named William Sealy Gosset was hired to work at the Guinness brewery in Dublin, Ireland. At the time, Guinness was in the habit of hiring the leading chemists from Oxford. Fortunately for Guinness, Gosset also had earned a degree in mathematics from the university. Gosset proved to be an excellent administrator and eventually became head of greater London operations.  Although Gosset was initially hired for his chemistry background, he quickly realized the importance mathematics and statistics could bring to the beer making process. In his first published paper, he demonstrated the use of the Poisson probability distribution to solve the problem of counting yeast cells for consistent measurement in beer making (for those readers unfamiliar with yeast, yeast is constantly multiplying and dividing, thus, an accurate count of the number of yeasts cells in a jar is near impossible). Gosset’s method of modeling yeast cells using a probability distribution allowed the factory to make more accurate assessments of the concentration of beer cells, ultimately producing a much more consistent brew (so when your Guinness tastes just as refreshingly delicious as the time before, be thankful that Gosset was so smart!).

As impressive as that first paper was, it was not Gosset’s biggest contribution to science. In a 1908 paper entitled “The Probable Error of the Mean,” Gosset addressed the issue of making statistical inferences from small samples (as Gossett needed to do for small samples of beer). At the time, the leading statistical methods relied on very large samples of data.  Gosset’s experience at Guinness let him to conclude that large sample sizes were not the norm in science; Gosset wanted to address the issue of making conclusions about the population in question if sample sizes are small. As a result, Gosset developed the t-test to test hypotheses about mean differences using small samples. This remarkable contribution brought about “small sample theory” and Gosset’s test became the foundation for modern tests of statistical significance.

If Gosset developed the method, why is it called Student’s t-test?

At the time, Guinness did not allow employees to publish papers. Some statistical historians (including Salsburg) claim the reason for the publishing ban was because a former employee of Guinness published some trade secrets in the past, thus necessitating the need for a rule banning all publications. Others argue that Guinness imposed this rule so that consumers would think of beer making as an artistic craft rather than a scientific process. Regardless for the reason, Gosset was forced to publish a under a pen name, Student, and consequently, Gosset’s innovative method became known as “Student’s t-test”.

At the time of its initial publication, Gosset’s seminal article, “The Probable Error of the Mean,” was not  celebrated or appreciated. It took the efforts of another famous statistician, Sir Ronald Fisher, to recognize the importance of the work and  bring the t-test into the modern statistical paradigm. Fisher made  three important advancements to  Gosset’s work; he (1) proved the t-test, (2) embedded the t-test into a unified framework for testing statistical significance, and 3) transformed Gosset’s “z –score” (with Gosset’s input) into the version of the “t-score” we use today.

So what exactly is Student’s  t-test?

Before delving into Student’s t-test, it is important  to understand a few essential terms:

Mean: The mean is more commonly known as an arithmetic average. It is calculated by adding all the scores in a sample or population and dividing by the number of scores.

Variance and Standard Deviation:  At the most basic level, variance and standard deviation are measures of dispersion. They answer the question: On average, how far away from the mean are the data? The standard deviation is simply the square root of the variance.

Population:  The set of all individuals of interest in a particular study.

Sample: Set of individuals selected from the population intended to represent the population in a research study.

Standard Error: The notion of the standard error can be difficult to understand. The standard error is a measure of how different estimates would be if we completed a study an infinite amount of times. For example, imagine we were interested in determining the average height of residents of the state of Arizona. Our population would be residents of the State of Arizona. We could randomly sample 200 residents and compute their average height. If we did it again, and randomly sampled another 200 people, and computed their average height, we’d get a slightly different mean estimate. Because each time we are getting a slightly different sample from the same population, we wouldn’t necessarily expect any given estimate of the average to exactly equal the mean of the population. If we sampled repeatedly and estimated the mean, we would end up with distribution of mean estimates.  Even though we don’t expect any two sampled means to be exactly the same, the mean of all the estimates would exactly equal the mean of the population. The standard error, is just an estimate of the standard deviation that we would theoretically expect to get if we sampled an infinite amount of times.  In simple terms, the standard error represents how stable we expect an estimate to be with repeated sampling and estimation. Because we never actually observe the standard error, we must estimate it. In terms of mean estimates, the standard error is estimated using information about the sample standard deviation and number of observations.

Now, let’s return to Student’s t-test. Student’s t-test has a wide variety of applications. Most generally the t-test is a statistic used to test hypotheses about mean differences. In the most basic test, we test the hypothesis that the sample mean is different from an unknown population mean when the standard deviation is unknown. We calculate the t-statistic as follows:

In the above expression, we are often testing the hypothesis that the population mean is zero (this is the test I will assume for the remainder of the example).  In other words, we are testing the hypothesis that the sample mean differs from zero.

Thus, assuming we are testing whether our observed mean differs from a population with a mean of zero, the t-statistic becomes:

Now that we have our t-statistic, what do we do?  We use the t-distribution (example shown below) to determine if we think our sample came from a population with a mean of zero.

Each t-distribution is specific to the number of degrees of freedom (i.e. the distribution changes based on the number of participants in the sample). We use the appropriate t-distribution, based on the number of people sampled, to determine which values of t you would expect to see if mean differences are simply due to random sampling (chance). What do I mean by “differences due to random sampling”? Recall from the definition of the standard error the mean of any given sample may not exactly match the population mean due to error inherent in the fact that our sample is only a subset of the population. In fact, there is a reasonable range of mean estimates that  we expect. We use the t-test to determine this reasonable range.  The horizontal axis of the t-distribution show a range of t-values. The vertical axis is the likelihood of observing a particular t-score (technically called the “probability density”). The area under the curve can be used to compute the probability of any given t-value. Values of t close to the center of the distribution, where the curve is the highest, represent very likely t-values that we would expect from a population with a mean of zero, whereas values of t near the tails are less likely. Thus, we can use our observed value of t to determine the probability that the sample mean came from a population with a mean of zero. We compute this probability by determining the area under the curve to the left of negative t and to the right of positive t (shown in red above). As an aside, this is an example of a two-tailed probability test. In layman’s terms, we are just testing whether the sample is different from zero without any preconceived notion of whether the difference will be higher or lower than zero. Sometimes you will see reference to a one-tailed test where we only consider probability on one end of the distribution. In a tw0-tailed test, the area under the curve in red represents the probability that our particular sample came from the hypothesized population with a mean of zero and we often call this value the “p-value”.  As a general rule of thumb, researchers will often determine a test statistically significant when the red area under the curve is less than or equal to .05 (Note: I hate this rule of thumb and will be blogging about this in a future entry).  In other words, assuming requisite assumptions are met*, we can determine the value of t we would expect that gives us only a 5% probability of obtaining a mean estimate as extreme as the one observed, assuming the null hypothesis is true (in this case the null hypothesis was that the population has a mean of zero). When the probability is very low, we reject the null hypothesis providing evidence that the alternative hypothesis (the sample’s population mean was not zero) is plausible.

And that, my friends, is one example of how researchers might apply student’s t-test.

*Assumptions of the t-test include independence of observations and normally distributed data.

The Phoenix New Times recently released its 2012 Best of Phoenix issue. This time it has a “science-y” twist that is just so cool called “Scientific Phoenix.” They even created an interactive periodic table for each elemental category. Click on the picture below to see the interactive chart.

Many of the categories have a science related theme like: Best Local Sci-Fi Author, Michael Stackpoole; Best Place to See a Dinosaur, Arizona Museum of Natural History; Best Brain Surgeon, Dr. Robert Spetzler of the Barrow Neurological Institute. It also includes tips on where to go to get the Best Science Experiment on an Ice Cream Cone, which is Sub Zero Ice Cream, where they flash-freeze the dessert with liquid nitrogen. There are all sorts of cool categories that mention nerd nirvana topics like rockets, robots and outer space. Check them out!

Recently a local news outlet reported about a study that claims that the type of beer that a person drinks directly correlates with which political party they vote for and how likely they are to vote. The outlet claiming to be “skeptical” took a version of a similar poll on Mill Ave in Tempe. The video, which can be found here, shows the newscaster asking a whole four people about their beer and political preferences. Despite the extremely small sample size and a whole host of other biases that were not accounted for, the newscaster was still able to find one outlier, who was a democrat that likes a “republican” beer.

I’m pretty sure by their tone the newscasters on this story weren’t taking it seriously and saw it as a fluff piece, but I think this is a great opportunity to demonstrate how skepticism really works. Let’s start with the original story, which shows a graph that shows how a particular brand of beer is preferred by democrats or republicans and how likely they are to vote.

The first question I wanted to ask was what about people who don’t drink beer? How are they counted? The second question is what about people who vote for candidates in other parties? Unfortunately the data that was used to make the graph doesn’t seem to be publicly available. All we do know is that 200,000 Americans were interviewed about their beer and political preferences. Without knowing the methodology of this study a lot more questions come into play. For example: How were these interviewees chosen? If they were chosen based on primary election voters, then the data can be skewed toward people who identify as either republican or democrat. Also, how was beer availability controlled? Not all the beers listed would be available in all regions, for example Yuengling isn’t available within 100 miles of Phoenix.