DataCamp
Course Outline

Recap hypothesis testing

50xp

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

    Take Hint (-15xp)