Data types you can analyze with a hypothesis test

Hypothesis tests can be used to evaluate many different parameters of a population. Each test is designed to evaluate a specific population parameter, and the type of population dictates the appropriate type of data. Knowing the population parameter of interest and the appropriate data type can help you choose the most appropriate test.
Continuous Data

Continuous data has an uncountable number of values between any two values. You should use continuous data when you want to make inference about a population mean, median, standard deviation, or variance.

When you measure a characteristic of a part or process, such as length, weight, or temperature, you usually obtain continuous data. Continuous data often includes fractional (or decimal) values. For example, a quality engineer wants to determine whether the mean weight differs from the value stated on the package label (500 g). The engineer samples cereal boxes and records their weights.

Bernoulli Data (also called binomial data)

You should use Bernoulli data to make inference about proportions or percentages. Bernoulli data is often referred to as binomial data because a Bernoulli population can be interpreted as a binomial population.

When you classify an item, event, or person into one of two categories you obtain binomial data. The two categories should be mutually exclusive, such as yes/no, pass/fail, or defective/nondefective. For example, engineers examine a sample of bolts for severe cracks that make the bolts unusable. They record the number of bolts that are inspected and the number of bolts that are rejected. The engineers want to determine whether the percentage of defective bolts is less than 0.2%.

Poisson Data

You should use count data to make inferences about a rate of occurrence of an event of interest.

When you count the presence of a characteristic, result, or activity over a certain amount of time, area, or other length of observation, you obtain Poisson data. Poisson data are evaluated in counts per unit, with the units the same size. For example, inspectors at a bus company count the number of bus breakdowns each day for 30 days. The company wants to determine the daily rate of bus breakdowns.