Data considerations for Bootstrapping for 1-Sample Mean

To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.

The data must be continuous, such as the weights of packages

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.

The sample data should be selected randomly

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.

Each observation should be independent from all other observations

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?.

The sample size should not be small

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.

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