To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.
If you know the standard deviation of the population, use 1-Sample Z because the Z-test has more power than the t-test.
Continuous data has an infinite number of values between any two values.
If your data contain counts, such as the number of defects per unit, use 1-Sample Poisson Rate. If your data classify each observation into one of two categories, such as pass/fail, use 1 Proportion. For more information on data types, go to Data types you can analyze with a hypothesis test.
If your sample size is greater than 20 and the underlying distribution is unimodal and continuous, the hypothesis test performs appropriately even if the data are mildly skewed. If your sample size is less than 20, you should graph the data to check for skewness and unusual observations. If the data is severely skewed or has many unusual observations, use caution when you interpret the results.
In statistics, random samples are used to make generalizations, or inferences, about a population. If your data are not collected randomly, your results may not represent the population. For more information, go to Randomness in samples of data.
If you have paired or dependent data, such as measurements of a bearing taken with two different calipers, use Paired t instead. For more information, go to How are dependent and independent samples different?.