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mathisfun.com's Standard Normal Distribution Table

Now, I am immediately suspicious of a website entitled "MathIsFun" (I prefer the soft sell...like promising teaching aids for statistics that are, say, not awful and boring). That being said, t his app. from mathisfun.com  may be an alternative to going cross-eyed while reading z-tables in order to better understand the normal distribution. mathisfun.com With this little Flash app., you can select z-scores and immediately view the corresponding portion of the normal curve (either from z = 0 to your z, up to a selected z, or to the right of that z). Above, I've selected z = 1.96, and the outlying 2.5% of the curve is highlighted.  Now, this wouldn't work for a paper and pencil exam (so you would probably still need to teach students to read the paper table) but I think this is useful in that it allows students to IMMEDIATELY see how z-scores and portions of the of the curve co-vary. 

Washington Post's "What your beer says about your politics"

Robinson & Feltus, 2014 There appears to be a connection between political affiliation, likelihood to vote, and preferred adult beverage. If you lean right and drink Cabernet Savignon, you are more likely to vote than one who enjoys "any malt liquor" and leans left.  This Washington Post story summarizes data analysis performed by the  National Media Research Planning and Placement . NMRPP got their data from market research firm Scarborough . There is also a video embedded in the Washington Post story that summarizes the main findings. I think this is a good example of illustrating data as well as data mining pre-existing data sets for interesting trends. And beer.

Hall vs. Florida: IQ, the death penalty, and margin of error (edited 5/27/14)

Here is Think Progress' story about a U.S. Supreme Court case that hinges on statistics. The case centers around death row inmate Freddy Lee Hall. He was sentenced to death in Florida for the murder of Karol Hurst in 1978. This isn't in dispute. What is in dispute is whether or not Hall qualifies as mentally retarded and, thus, should be exempt from the death penalty per Virginia vs. Atkins . So, this is an example relevant to any number of psychology classes (developmental, ethics, psychology and the law, etc.). It is relevant to a statistics class because the main thrust of the argument has to do with the margin of error associated with the IQ test that designated Hall as having an IQ of 71. In order to qualify as mentally retarded in Florida, an individual has to have an IQ of 70 or lower. So, at first blush, Hall is out of luck. Until his lawyers bring up the fact that the margin of error on this test is +/- 5 points. This is a good example of confidence intervals/marg...

UPDATE: The Knot's Infographic: The National Average Cost of a Wedding is $28,427

UPDATE: The average cost of a wedding is now $33,391, as of 2017 . Here is the most up to date infographic: Otherwise, my main points from the original version of this survey are still the same: 1) To-be-weds surveyed for this data come were users of a website used to plan/discuss/squee about pending nuptials. So, this isn't a random survey. 2) If you look at the fine print for the survey, the average cost points quoted come from people who paid for a given service. So, if you didn't have a reception band ($0 spent) your data wasn't used to create the average. Which probably leads to inflation of all of these numbers. _________________________________________ Original Post: This infographic describes the costs associated with an "average" wedding. It is a good example non-representative sampling and bending the truth via lies of omission. For the social psychologists in the crowd, this may also provide a good example of persuasion by establishing ...

Nature's "Policy: Twenty tips for interpreting scientific claims" by William J. Sutherland, David Spiegelhalter, & Mark Burgman

This very accessible summary lists the ways people fib with, misrepresent, and overextend data findings. It was written as an attempt to give non-research folk (in particular, law makers), a cheat sheet of things to consider before embracing/rejecting research driven policy and laws. A sound list, covering plenty of statsy topics (p-values, the importance of replication), but what I really like is that they article doesn't criticize the researchers as the source of the problem. It places the onus on each person to properly interpret research findings. This list also emphasizes the importance of data driven change.

Baby Name Wizard's NameVoyager

UPDATE (12/8/23): YOOOOOOOOO if you got to this post, I suggest that you check out this update for a up-to-date link to this tool. Here is the  Baby Name Wizard's NameVoyager , which provides illustrations of trends in baby names, using data from the 1880s to the present. It is a good tool for demonstrating why graphs can be more engaging than tables when presenting data. When I use this in class, I compare the NameVoyager data display to more  traditionally presented data from the the Social Security Agency . Additionally, I teach in a computer lab, so my students were able to search for their own names, which makes the example more self relevant. Yup. I am one of many, many Jessicas that are around my age.

NPR's "Will Afghan polling data help alleviate election fraud?"

This story details the application of American election polling techniques to Afghanistan's fledgling democracy. Essentially, international groups are attempting to poll Afghans prior to their April 2014 presidential elections as to combat voter fraud and raise awareness about the election. However, how do researchers go about collecting data in a country where few people have telephones, many people are illiterate, and just about everyone is weary about strangers approaching them and asking them sensitive questions about their political opinions? The story also touches on issues of social desirability as well as the decisions  a researcher makes regarding the kinds of response options to use in survey research. I think that this would be a good story to share with a cranky undergraduate research methods class that thinks that collecting data from the undergraduate convenience sample is really, really hard. Less snarkily, this may be useful when teaching multiculturalism or ...

A.V. Club's "Shirley Manson takes BuzzFeed's "Which Alt-Rock Grrrl Are You?" quiz, discovers she's not herself"

Lately, there have been a lot of quizzes popping up on my Facebook feed ("What breed of dog are you?", "What character from Harry Potter are you?"). As a psychologist who tinkers in statistics, I have pondered the psychometric properties of such quizzes and concluded that these quizzes where probably not properly vetted in peer-reviewed journals. Now I have a tiny bit of evidence to support that conclusion. What better way to ensure that a scale is valid than by using the standard of concurrent validity (popular in I/O psychology)? This actually happened when renowned Shirley Manson Subject Matter Expert, Shirley Manson, lead singer of the band Garbage, took the "Which Alt-rock Grrrl are you?" quiz and she didn't score as herself (as she posted on Facebook and reported by A.V. Club ). From Facebook, via A.V. Club An excellent example of an invalid test (or concurrent validity for you I/O types).

Anecdote is not the plural of data: Using humor and climate change to make a statistical point

Variations upon a theme...good for spicing up a powerpoint...inspired by living in the #1 snowiest city (population > 100K, 2014) in the United States. property of xkcd.com https://thenib.com/can-t-stand-the-heat-4d5650fd671b

Time's "Can Time predict your politics?" by Jonathan Haidt and Chris Wilson

This scale , created by Haidt and Wilson, predicts your political leanings based upon seemingly unrelated questions. Screen grab from time.com You can use this in a classroom to 1) demonstrate interactive, Likert-type scales, 2) face validity (or lack there of). I think this would be 3) useful for a psychometrics class to discuss scale building. Finally, the update at the end of the article mentions 4) both the n-size and the correlation coefficient for their reliability study, allowing you discuss those concepts with students. For more about this research, try yourmorals.org

NPR's "In Pregnancy, What's Worse? Cigarettes Or The Nicotine Patch?"

This story discusses the many levels of analysis required to get to the bottom of the hypothesis stated in the title of this story. For instance, are cigarettes or the patch better for mom? The baby? If the patch isn't great for either but still better than smoking, what sort of advice should a health care provider give to their patient who is struggling to quit smoking? What about animal model data? I think this story also opens up the conversation about how few medical interventions are tested on pregnant women (understandably so), and, as such,  researchers have to opt for more observational research studies when investigating medical interventions for protected populations.

Shameless self-promotion 2

Here is a link to a recent co-authored publication that used Second Life to teach students about virtual data collection as well as the broader trend in psychology to study how virtual environments influence interpersonal interactions. Specifically, students replicated evolutionary psychology findings using Second Life avatars. We also discuss best practices for using Second Life in the class room as well as our partial replication of previously established evolutionary psychology findings (Clark & Hatfield, 1989, Buss, Larson, Weston, & Semmelroth, 1992).

Changes in standards for data reporting in psychology journals

Two prominent psychology journals are changing their standards for publication in order to address several long-standing debates in statistics (p-values v. effect sizes and point estimates of the mean v. confidence intervals). Here are the details for changes that the Association for Psychological Science is creating for their gold-standard publication, Psychological Science, in order to improve the transparency in data reporting. Some of the big changes include mandatory reporting of effect sizes, confidence intervals, and inclusion of any scales or measures that were non-significant. This might be useful in class when describing why p-values and means are imperfect, the old p-value v. effect size debate, and how one can bend the truth with statistics via research methodology (and glossing over/completely neglecting N.S. findings). These examples are also useful in demonstrating to your students that these issues we discuss in class have real world ramifications and aren't be...