# Data considerations for Bootstrapping for 1-sample function

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

To analyze a mean, median, sum, variance, or standard deviation, the data must be numeric
You must have continuous data, such as the weights of packages, or discrete data, such as the number of complaints. If you have binary data, such as yes/no or pass/fail, analyze a proportion.
To analyze a proportion, the data can contain only two categories, such as pass/fail and 1/0
If you have continuous or discrete data, such as length, weight, or number of complaints, select a function for continuous or discrete data, such as the mean.
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.