**Statistical stability **is how well the results of your study or experiment hold up.

More specifically, it’s a measure of how well you control for random errors in your study.

Statistical stability can be defined more precisely for specific fields. For example, let’s say you’re working with signal-to-noise ratio. Josselin Garnier and George Papanicolaou, in the book *Passive Imaging with Ambient Noise*, describe it as meaning

“…high signal-to-noise ratio of the quantity considered, including the image itself.”

## How Do I Make Sure My Results Have Statistical Stability?

Ways to ensure statistical stability to test your null hypothesis include:- p-values. A p value is used in hypothesis testing to support or reject the null hypothesis. It is the evidence against a null hypothesis. In general, the smaller the p-value the better.
- confidence intervals. For example, you might report a 95% confidence interval with your results.

## References

Aschengrau, A. & Seage, G. Essentials of Epidemiology in Public Health. Retrieved September 18, 2019 from: https://books.google.com/books?id=QelGjoKOWTAC

Josselin Garnier and George Papanicolaou. *Passive Imaging with Ambient Noise*,Retrieved September 18, 2019 from: https://books.google.com/books?id=9jrzCwAAQBAJ

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*Statistical concepts explained visually* - Includes many concepts such as sample size, hypothesis tests, or logistic regression, explained by Stephanie Glen, founder of StatisticsHowTo.

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