[Link] eLife webinar on practical interpretation of statistical significance

I thought this webinar about p-values was quite good, and encouraged adopting a critical stance on what a significant results indicates. It’s given by a clinical researcher, so it’s quite practical and doesn’t dive to deep into maths.

The following diagram (from an economist article) is shown in the talk and good illustration of the uphill battle in reproducible research - even if correct statistical practices are being followed (significance at 5% and power at 80%), if 1 in 10 hypothesis are correct then around a third of the significant results are still false positives and won’t reproduce! And any studies using questionable research practices are only going to make that worse…



thanks gavin. related, i was impressed enough with asa’s recommendation on p-values that i started translating it into indonesian.


due to google algorithm, or some other means, i found the following link which apparently takes that recommendation with a grain of salt, which i mostly agree. :slight_smile:


Yeah, there are pros and cons on both sides of this. From those blog posts it seems that the recommendations of the ASA 2016 statement are most reasonable (and I think they are emphasized in the webinar I link to):

  1. P -values can indicate how incompatible the data are with a specified statistical model.
  2. P -values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone.
  3. Scientific conclusions and business or policy decisions should not be based only on whether a p -value passes a specific threshold.
  4. Proper inference requires full reporting and transparency. P -values and related analyses should not be reported selectively. Conducting multiple analyses of the data and reporting only those with certain p -values (typically those passing a significance threshold) renders the reported p -values essentially uninterpretable.
  5. A p -value, or statistical significance, does not measure the size of an effect or the importance of a result.
  6. By itself, a p -value does not provide a good measure of evidence regarding a model or hypothesis.

I also recall reading (or hearing) that the p-value was currently the only thing holding back a floodgate of irreproducible findings and I agree that the 2nd ASA statement calling for the removal of the p-value goes to far.

Maybe the key point is that while frequentist statistics are mathematically sound, most people don’t really understand what significant results imply. I think point 2 above is a common problem for biologists - the assumption is that p<0.05 means there is a >95% chance of the two sampling populations being different, while it actually means there is a <5% chance of the observed difference occurring if the populations are the same.