Researchers Continue to Consistently Misinterpret p-values

For as long as there have been p-values there have been people misunderstanding p-values. Their nuanced definition eludes many researchers, statisticians included, and so they end up being misused and misinterpreted. The situation recently prompted the American Statistical Association (ASA)  to produce a statement on p-values.[1] Yet, they are still widely viewed as the most important bit of information in an empirical study, and careers are still built on ‘statistically significant’ findings. A paper in Management Science,[2] recently reported on Andrew Gelman’s blog,[3] reports the results of a number of surveys of top academics about their interpretations of the results of hypothetical studies. They show that these researchers, who include authors in the New England Journal of Medicine and American Economic Review, generally only consider whether the p-value is above or below 0.05; they consider p-values even when they are not relevant; they ignore the actual magnitude of an effect; and they use p-values to make inferences about the effect of an intervention on future subjects. Interestingly, the statistically untrained were less likely to make the same errors of judgement.

As the ASA statement and many, many other reports emphasise, p-values do not indicate the ‘truth’ of a result, nor do they imply clinical or economic significance, they are often presented for tests that are completely pointless, and they cannot be interpreted in isolation of all the other information about the statistical model and possible data analyses. It is possible that in the future the p-value will be relegated to a subsidiary statistic where it belongs rather than the main result, but until that time statistical education clearly needs to improve.

— Sam Watson, Research Fellow

References:

  1. Wasserstein RL & Lazar NA. The ASA’s Statement on p-Values: Context, Process, and Purpose. Am Stat. 2016; 70(2). [ePub].
  2. McShane BM & Gal D. Blinding Us to the Obvious? The Effect of Statistical Training on the Evaluation of Evidence. Manage Sci., 2015; 62(6): 1707-18.
  3. Gelman A. More evidence that even top researchers routinely misinterpret p-values. Statistical Modeling, Causal Inference, and Social Science. 26 July 2016.
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