To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.
Continuous data has an infinite number of values between any two values.
If your data classify each observation into one of two categories, such as pass/fail, use Bootstrapping for 1-Sample Proportion. For more information on data types, go to Data types you can analyze with a hypothesis test.
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.
For observations to be independent, the value of a particular observation does not depend on any previous observation. If your observations are not independent, your results may not be valid. For more information, go to How are dependent and independent samples different?.
If your sample size is small, the resampling results may be unreliable. To ensure that your results are valid, collect a medium to large sample. An adequate sample size depends on the characteristics of the data. Use the histogram to determine whether your sample size is large enough.