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Albert Steppi, Matt Haberland - Resampling and Monte Carlo Methods in SciPy.stats | SciPy 2023
Discover how Resampling and Monte Carlo methods in SciPy.stats can be used to perform hypothesis tests, create confidence intervals, and determine statistical significance in this informative conference talk.
- Monte Carlo methods are used in statistics to make probabilistic statements about complex systems.
- Resampling is a technique used to create a null distribution by repeatedly resampling from the original dataset.
- Bootstrapping is a type of resampling where a dataset is resampled with replacement to create a null distribution.
- A permutation test is a statistical test that uses a null distribution to determine the probability of observing a result as extreme or more extreme as the observed result.
- The bootstrap distribution can be used to create a confidence interval for a statistic.
- A confidence interval is an interval that has a certain probability of containing the true value of a parameter.
- The probability of observing a result as extreme or more extreme as the observed result is known as the p-value.
- Hypothesis tests can be used to test a null hypothesis that a treatment has no effect, and the alternative hypothesis that the treatment does have an effect.
- The null distribution is used to determine the p-value, which is the probability of observing a result as extreme or more extreme as the observed result under the null hypothesis.
- The p-value is commonly used as a threshold to determine whether the result is statistically significant, with a typical threshold of 0.05.
- Resampling and Monte Carlo methods can be used to perform hypothesis tests and create confidence intervals.
- Bootstrapping can be used to create a null distribution, which can then be used to perform a permutation test.
- A permutation test is a statistical test that uses a null distribution to determine the probability of observing a result as extreme or more extreme as the observed result.
- The p-value is a measure of the probability of observing a result as extreme or more extreme as the observed result under the null hypothesis.
- The bootstrap distribution can be used to create a confidence interval for a statistic.
- A confidence interval is an interval that has a certain probability of containing the true value of a parameter.
- The probability of observing a result as extreme or more extreme as the observed result is known as the p-value.
- Hypothesis tests can be used to test a null hypothesis that a treatment has no effect, and the alternative hypothesis that the treatment does have an effect.
- The null distribution is used to determine the p-value, which is the probability of observing a result as extreme or more extreme as the observed result under the null hypothesis.
- The p-value is commonly used as a threshold to determine whether the result is statistically significant, with a typical threshold of 0.05.