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Based on the same random sample and same test statistic, will the p value remain the same even when the distribution of test statistic under null hypothesis is changed. Or is this scenario true only in case of a distribution and it's asymptotic distribution say t and normal?

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    $\begingroup$ What do you mean by "distribution of null hypothesis is changed"? Are you asking if testing different hypothesis could lead to different p-values? The answer is pretty obvious... $\endgroup$ – Tim Oct 31 '19 at 18:29
  • $\begingroup$ I meant distribution of test statistic under null hypothesis $\endgroup$ – Harry Nov 1 '19 at 3:41
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The p-value depends on the sampling distribution of the statistic under the null hypothesis. If one null hypothesis implies a different distribution (i.e., with a different mean or variance) from another null hypothesis, then the p-values will change. If the standard error of the statistic depends on the distribution under the null hypothesis, then the p-value, which depends on the standard error, will differ.

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Your question has a lot to do with the subtle nature of what a p-value actually is and how it is interpreted. It's not exactly correct to say that the null hypothesis has a distribution at all; as @Noah correctly points out, the p-value relies on the sampling distribution of the test statistic: in frequentist inference, the sampling distribution is constructed partly from a hypothetical, user-supplied "null hypothesis" (such as 'the true mean $\mu=2$' in a one-sample test or '$\mu_1 - \mu_2 = 0$' in a two-sample test) that involves a statement concerning one or more of the parameters of a probability distribution. Other parameters needed to fully specify the distribution (such as the variance $\sigma^2$ in a model where normality is assumed) might also be hypothesized by the investigator, or they may be estimated using the observed data. Most likely, a technique such as maximum likelihood estimation is used to fix the distributional parameters at their "most likely" values.

What this means in regards to your question is that the p-value relies on a distribution which is partly assumed by the researcher, partly determined by the nature of the researcher's hypothesis, and partly estimated from the data. The p-value may not stay the same if either the researcher's basic assumptions or hypotheses change.

In general, however, formulating a null hypothesis is not an arbitrary process and many statistical tests are robust to mild deviations from normality (which is a common assumption). It is hard to imagine a scenario where a well-formed hypothesis changes within an experiment or a researcher makes an assumption such as normality without first having some evidence that this is a safe assumption to make.

With this in mind, I generally think of frequentist inference as being fully determined by the observed data, while knowing that this isn't exactly true. However, as long as a researcher takes the time to understand their hypotheses and assumptions, there should be no need to change them. IMHO it is quite a slippery slope to think of p-values as things that can be manipulated with a cleverly-formulated null hypothesis or a subtle assumption.

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  • $\begingroup$ Excellent answer. $\endgroup$ – Noah Oct 31 '19 at 20:25

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