Can p-values be misleading?

Misuse of p-values is common in scientific research and scientific education. p-values are often used or interpreted incorrectly; the American Statistical Association states that p-values can indicate how incompatible the data are with a specified statistical model.

How p-values are misinterpreted?

Another common misunderstanding of p-values is the belief that the p-value is “the probability that the null hypothesis is true”. This is the reverse conditional probability from the one considered in frequentist inference (the probability of the data given that the null hypothesis is true).

Why are p-values not useful?

Indeed, as Marden (2000) points out, the p value is not very useful with large sample sizes. Because almost no null hypothesis is exactly true (Tukey, 1991), when sample sizes are large enough almost any null hypothesis will have a tiny p value.

What is false about P value?

A positive is a significant result, i.e. the p-value is less than your cut off value, normally 0.05. A false positive is when you get a significant difference where, in reality, none exists. As I mentioned above, the p-value is the chance that this data could occur given no difference actually exists.

Why is p-value criticized?

4.4 P-Values Do not Overstate Evidence Against Hypotheses—People Do. Among fair criticisms of P-values are that they are too easily confused with posterior probabilities, and that they are distortive evidence measures that need logarithmic transformation to gauge properly (e.g., Bayarri and Berger 1999.

Why are p-values controversial?

The controversy exists because p-values are being used as decision rules, even though they are data-dependent, and hence cannot be formal decision rules. Incorrectly using p-values as decision rules effectively eliminates the idea of a valid decision rule from a test, and therefore invalidates the decision.

Why is p value criticized?

What do you do if p-value is not significant?

A p-value higher than 0.05 (> 0.05) is not statistically significant and indicates strong evidence for the null hypothesis. This means we retain the null hypothesis and reject the alternative hypothesis. You should note that you cannot accept the null hypothesis, we can only reject the null or fail to reject it.

Is a high p-value good or bad?

A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis. A large p-value (> 0.05) indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis. Always report the p-value so your readers can draw their own conclusions.

Why the p-value culture is bad?

A consequence of the dominant P-value culture is that confidence intervals are often not appreciated by themselves, but the information they convey are transformed into simplistic terms of statistical significance. For example, it is common to check if the confidence intervals of two mean values overlap.

What are the uses and limitations of p-value?

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.

Why do we care about p-value?

It just helps you understand how rare the results are. It tells you how often you’d see the numerical results of an experiment — or even more extreme results — if the null hypothesis is true and there’s no difference between the groups. If the p-value is very small, it means the numbers would rarely (but not never!)