Final answer:
In hypothesis testing, you do not need the population distribution for a t-test. You need a null hypothesis and sample data to evaluate if there is significant evidence to reject the null hypothesis.
Step-by-step explanation:
In hypothesis testing, you do not need the population distribution for a t-test. What you need is a null hypothesis, which could be a theory, logical prediction, or wishful thinking. The null hypothesis assumes that there is no significant difference or relationship between variables in the population.
When conducting a t-test, you gather a sample from the population and evaluate the sample data to determine if it provides enough evidence to reject the null hypothesis. You compare the observed sample results to what would be expected under the assumption that the null hypothesis is true.
For a t-test, you typically assume that the sample comes from a simple random sample, and the population is approximately normally distributed, or the sample size is large enough. The t-test allows you to make inferences about the population based on the sample data.