Hypothesis tests can be used to evaluate many different parameters of a population. Each test is designed to evaluate a parameter associated with a certain type of data. Knowing the difference between the types of data, and which parameters are associated with each data type, can help you choose the most appropriate test.

- Continuous Data
- You will have continuous data when you evaluate the 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.
- Binomial Data
- You will have binomial data when you evaluate a proportion or a percentage.
- 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 will have Poisson data when you evaluate a rate of occurrence.
- 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.

If you can choose which type of data to collect, collect continuous data. Continuous data provides more detailed information and often gives a hypothesis test more power.