Did you know that American Statistical Association (ASA) made a disclaimer about the proper use of p-values? Moreover, ASA (the oldest scientific association in the USA) claimed that:
- P-values can indicate how incompatible the data are with a specified statistical model.
- 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.
- Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.
- Proper inference requires full reporting and transparency.
- A p-value, or statistical significance, does not measure the size of an effect or the importance of a result.
- By itself, a p-value does not provide a good measure of evidence regarding a model or hypothesis.
With such a disclaimer, from my statistical perspective I gave a talk at ICFES about the use (misuse and abuse) of p-values and how to face this new reality. In the end, I consider that p-values and hypothesis testing have several disadvantages in this information era. With Petabytes of data generated every day, sample sizes influence directly on p-values, and decisions taken from this perspective may be misleading.