Recap hypothesis testing

For those of you that have taken basic statistics, hypothesis testing is familiar. Let's however recap it briefly.

When we are testing between two competing hypotheses, a null hypothesis H0 and an alternative hypothesis H1, we generally assume that the null hypothesis is true unless the data shows a strong indication that this is not the case. By doing hypotheses testing, we test the probability of finding a sample statistic given that the null hypothesis is true. If the null hypothesis is true, the difference between a sample statistics and the population parameter is due to sampling error, that is, fluctuations in the sample from the population. However, if the probability of finding a sample statistic as extreme as ours under the null hypothesis is very small, we generally reject the null hypothesis.

Imagine we have found a p value of 0.30 called p1 and another p value of 0.02 called p2, do these p values indicate strong evidence or weak evidence in favour of the null hypothesis?

Possible Answers

p1 indicates strong evidence in favour of the null hypothesis; p2 indicates strong evidence in favour of the null hypothesis
p1 indicates weak evidence in favour of the null hypothesis; p2 indicates strong evidence in favour of the null hypothesis
p1 indicates strong evidence in favour of the null hypothesis; p2 indicates weak evidence in favour of the null hypothesis

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Recap hypothesis testing

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