Posts
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| How to torment your colleagues with statistics |
It turns out that researchers in many fields aren’t particularly good statisticians. So when they apply statistical tools to data they’ve collected, they often screw up the math or draw the wrong conclusions from their calculations.
In particular, researchers are prone to find statistical significance in results where there really isn’t any. So warns Steve Ziliak, an economics professor at Roosevelt University. Ziliak coauthored a book called The Cult of Statistical Significance wherein he warns that researchers frequently misuse the student T-test and p values. Ziliak combed through papers published in numerous prestigious economics, operations research, and medical journals. He found many instances of researchers using statistical significance as if it was the same as correlation...
An additional fun online tool is WebPlotDigitizer (apps.automeris.io/wpd/) which examines a plot you upload (as an image) to it and extracts the x and y values, among other things. Given a forest plot (a graphical display of estimated results from a number of scientific studies addressing the same question,), the tool also extracts other parameters such as means and confidence intervals. When given histograms or bar charts it figures percentages and calculates angles and distances from images.
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Recommended Reading
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| The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives (Economics, Cognition, And Society) |
