This is the most commonly used tool in inferential statistics. It combines the concepts of ziscores, probability, and the distribution of sample means to evaluate a hypothesis about a population.

1. State a hypothesis about a population that usually has to do with a population parameter.

2. Use the hypothesis to make a prediction about the sample that you are going to take. The sample is likely to have a certain amount of error.

3. Obtain a random sample of the population of a particular sample size, n, and obtain data from the sample.

4. Compare the sample data with the prediction that was made from the hypothesis. If the sample mean is consistent with the prediction, then it can be concluded that the hypothesis is reasonable and not if otherwise.

The goal is to determine what will happen to the population after the treatment: if the treatment has any effect it will add or subtract a constant to each individual's score. This means the population will have the same shape before and after the experiment.

Things that influence the outcome of a hypothesis test: 1) The variability of scores measured by the standard deviation or variance and 2) The number of scores in the sample, n, which influences the size of the variability of scores.