Keypoints

• Null hypothesis testing is a formal statistical procedure for making decisions about a population parameter based on a sampling statistic.

• A statistical hypothesis is an expectation or prediction about a population parameter.

• The null hypothesis is the statement that is being tested, specifying a 'no-effect' situation for the population parameter.

• The alternative hypothesis is a statement about the population parameter that contrasts with the null hypothesis, specifying a 'true-effect' situation for the population parameter.

• The null and alternative hypotheses are mutually exclusive.

• The null hypothesis can be rejected when the sample statistic is very unlikely under the null-model or not be rejected when not unlikely.

• If the null hypothesis is rejected this provides evidence for the alternative hypothesis.

In this practical you furthermore learned to apply the R function t.test() and prop.test() to test for a difference between

• a population mean and a prior value

• a population proportion and a prior value

You learned to use parameter-options to specify the significance level and the direction of the hypothesis test (two-sided, one-sided).