### Assumptions

When you perform *a hypothesis test of a single population mean **μ* using a Student's *t*-distribution (often called a *t*-test), there are fundamental assumptions that need to be met in order for the test to work properly. Your data should be a simple random sample that comes from a population that is approximately normally distributed. You use the sample standard deviation to approximate the population standard deviation. Note that if the sample size is sufficiently large, a *t*-test will work even if the population is not approximately normally distributed.

When you perform a *hypothesis test of a single population mean **μ* using a normal distribution (often called a *z*-test), you take a simple random sample from the population. The population you are testing is normally distributed or your sample size is sufficiently large. You know the value of the population standard deviation which, in reality, is rarely known.

When you perform a *hypothesis test of a single population proportion **p*, you take a simple random sample from the population. You must meet the conditions for a binomial distribution, which are the following: there are a certain number *n* of independent trials, the outcomes of any trial are success or failure, and each trial has the same probability of a success *p*. The shape of the binomial distribution needs to be similar to the shape of the normal distribution. To ensure this, the quantities *np* and *nq* must both be greater than five (*np* > 5 and *nq* > 5). Then the binomial distribution of a sample (estimated) proportion can be approximated by the normal distribution with *μ* = *p* and $\sigma =\sqrt{\frac{pq}{n}}$. Remember that *q* = 1 – *p*.