This NPR story is a summary of the decisively titled " The Mass Production of Redundant, Misleading, and Conflicted Systematic Reviews and Meta-analyses " authored by Dr. John Ioannidis. The NPR story provides a very brief explanation of meta-analysis and systematic reviews. It explains that they were originally used as a way to make sense of many conflicting research findings coming from a variety of different researchers. But these very influential publications are now being sponsored and possibly influenced by Big Pharma. This example explains conflicts of interest and how they can influence research outcomes. In addition to financial relationships, the author also cites ideological allegiances as a source of bias in meta-analysis. In addition to Dr. Ioannidis, Dr. Peter Kramer was interviewed. He is a psychiatrist who defends the efficacy of antidepressants. He suggests that researchers who believe that placebos are just as effective as anti-depressants tend to analy...

This NPR story is about a survey conducted by the folks at Sesame Street. And that survey asked parents and teachers about kindness. If kids are kind, if the world is kind, how they define kindness, etc.. The NPR story is a round about way of explaining how we operationalize variables, especially in psychology. And the survey itself provides examples of forced choice research questions and dichotomous responses that could have been Likert-type scales. The NPR Story: The Children's Television Workshop, the folks behind Sesame Street, have employees in charge of research and evaluation (a chance to plug off-the-beat-path stats jobs to your students). And they did a survey to figure out what it means to be kind when you are a kid. They surveyed parents and teachers to do so. The main findings are summarized here . Parents and teachers are worried that the world isn't kind and doesn't emphasize kind. But both groups think that kindness is more important than academic a...

Two scientists, Lazris and Rifkin, want to better illustrate the risks and benefits associated with preventative medicine. They do so by asking people to imagine theaters filled with 1,000 people, and describing the costs and benefits for different preventative procedures by discussing how many people in the theater will be saved or perish based on current efficacy data. One such video can be viewed here and illustrates the absolute and relative risks associated with mammography. They are attempting to demystify statistics and better explain the risks and benefits by showing an animated theater filled with 1,000 women, and showing how many women actually have their lives saved by mammograms (see screen shot below)... ...as well as the number of women who received false positives over the course of a life time... A screen shot of the video, which is trying a new way to illustrate risk. ...the video also illustrates how a "20% reduction in breast cancer deaths" ca...

Sometimes, we need to convince our students that taking a statistics class changes the way they think for the better. This example demonstrates that one seemingly simple skill, interpreting a scatter plot, is tougher than it seems. Pew Research conducted a survey on scientific thinking in America ( here is a link to that survey ) and they found that only 63% of American adults can correctly interpret the linear relationship illustrated in the scatter plot below. And that 63% came out a survey with multiple-choice responses! How to use in class: -Show your students that a major data collection/survey firm decided that interpreting statistics was an appropriate question on their ten-item quiz of scientific literacy. -Show your students that many randomly selected Americans can't interpret a scatter plot correctly. And for us instructors: -Maybe a seemingly simple task like the one in this survey isn't as intuitive as we think it is!

This interactive tool from Pew research illustrates left and right skew as well as median and longitudinal data. The x-axis indicates how politically consistent (as determined by a survey of political issues) self-identified republicans and democrats are across time. Press the button and you can animate data, or cut up the data so you only see one party or only the most politically active Americans. http://www.people-press.org/2014/06/12/section-1-growing-ideological-consistency/#interactive The data for both political part goes from being normally distributed in 1994 to skewed by 2014. And you can watch what happens to the median as the political winds change (and perhaps remind your students as to why mean would be the less desirable measure of central tendency for this example). I think it is interesting to see the relative unity in political thought (as demonstrated by more Republicans and Democrats indicating mixed political opinions) in the wake of 9/11 but more politicall...

I just used this example in class, and I realized that I never shared it on my blog. I really love this example of Type II error (and some other stuff, too). So here it goes. http://www.achievement.org/autodoc/page/mar1int-1

I've been using this example in class for a few years but never got around to blogging about it until now. It seems that the first chapter of every statistics class provides a boring explanation of what a variable is, and examples of variables, and operationalizing variables, and quantifying the abstract for the purposes of conducting statistical analyses. I try to make that boring topic funnier and applicable to real life via this post entitled "Boyfriend doesn't have ebola. Probably." from Allie Brosh, editor of Hyperbole and a Half . In this posting, she rips apart the good old FACES scale after a trip with her boyfriend to the ER.

Gleaned from multiple sources (FB, Pinterest, Twitter, none of these belong to me, etc.). Remember, if your students can explain why a stats funny is funny, they are demonstrating statistical knowledge. I like to ask students to explain the humor in such examples for extra credit points (see below for an example from my FA14 final exam). Using xkcd.com for bonus points/assessing if students understand that correlation =/= causation What are the numerical thresholds for probability?  How does this refer to alpha? What type of error is being described, Type I or Type II? What measure of central tendency is being described? Dilbert: http://search.dilbert.com/comic/Kill%20Anyone Sampling, CLT http://foulmouthedbaker.com/2013/10/03/graphs-belong-on-cakes/ Because control vs. sample, standard deviations, normal curves. Also,"skewed" pun. If you go to the original website , the story behind this cakes has to do w...

http://xkcd.com/1725/ This comic is another great example of allowing your student to demonstrate statistical comprehension by explaining why a comic is funny. What does the r^2 indicate? When would it be easy to guess the direction of the correlation?  More on that via this previous blog post .

I have shared Kristoffer Magnusson's fantastic visualizations of statistical concepts here previously ( correlation , Cohen's d ). Here is another one that helps to explain confidence intervals , and how the likelihood of an interval containing true mu varies based on interval size as well as the size of the underlying sample. The site is interactive in two ways. 1) The sliding bar at the top of the page allows you to adjust the size of the confidence interval, which you can read in the portion of the page labeled "CI coverage %" or directly above the CI ticker. See below. 2) You can also change the n-size for the samples the simulation is pulling. The site also reports back the number of samples that include mu and the number of samples that miss mu (wee little example for Type I/Type II error). How to use it in class: Students will see how intervals increase and decrease in size as you reset the CI percentage. As the sample size increases, the range ...

Take a look at this interactive, statistical decision making flow chart. I think that almost every statistics text includes a flow chart, but the interactive piece of this, and its ability to immediately provide the reader with information on the appropriate analysis AND software assistant is something your students can't get from paper versions of same. The flow chart is based on Andy Field's work. I discovered this tool via Reddit. I'm including that Reddit thread because the person that created the thread (commentor4) states that they also created the flow chart. So, you are lead through a series of questions (read this from the bottom up). After you provide the necessary information, the page provides you with a quick definition of the test you should conduct as well as links to instruction using popular statistical packages.

This syllabus for  PSY 607: Everything is Fucked ,  made the rounds last week. The syllabus is for a course that  purports  that science is fucked. The course readings are a list of articles and books that hit on the limitations of statistics and research psychology ( p -values, shortcomings of meta-analysis, misuse of mediation, replication crisis, etc.). PSY 607 isn't an actual class ( as author/psychologist/blogger Srivastava explains in this piece from The Chronicle ) but it does provide a fine reading list for understanding some of the current debates and changes in statistics and psychology.  Most of articles are probably too advanced for undergraduates but perfectly appropriate for teaching graduat e students about our field and staying up to date as instructors of statistics. Here is a link to the original blog post/syllabus. 

This study, which found a link between maternal Tylenol usage during pregnancy and ADHD, has been making the rounds, particularly in the Academic Mama circles I move in. Being pregnant is hard. For just about every malady, the only solution is to stay hydrated. With a compromised bladder. But at least pregnant women have Tylenol for sore hips and bad backs. For a long time, this has been the only safe OTC pain reliever available to pregnant women. But a recent research article has cast doubt on this advice. A quick read of this article makes it sound like you are cursing your child with a lifetime of ADHD if you take Tylenol. A nd this article has become click-bait fodder. But these findings have some pretty big caveats.  Harris published this reaction piece at NPR . It is a good teaching example of media hype vs. incremental scientific progress and the third (or fourth or fifth) variable problem. It also touches on absolute vs. relative risk. NOTE: There are well-documente...