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Why is confidence intervals better than hypothesis tests?
Confidence intervals gives us a range of possible values and an estimate of the precision for our parameter value. Hypothesis tests tells us how confident we are in drawing conclusions about the population parameter from our sample.
How do we calculate the P value for a permutation test?
To calculate the p-value for a permutation test, we simply count the number of test-statistics as or more extreme than our initial test statistic, and divide that number by the total number of test-statistics we calculated.
How is a confidence interval used to test a mean?
There is another way of testing a mean and that is by constructing a confidence interval about the true but unknown mean. General form of confidence intervals where the standard deviation is unknown
How are p.values and confidence intervals different?
But you should really keep confidence intervals and p.values strictly separated. The confidence interval is estimated based on bootstrapping within each sample (although it is biased by definition), the permutation test is done by permutations over the complete dataset. Those are two completely different things.
Which is an example of a permutation test?
Permutation tests are significance tests based on permutation resamples drawn at random from the original data. Permutation resamples are drawn without replacement, in contrast to bootstrap samples, which are drawn with replacement. Here is an example I did in R of a simple permutation test.
Why does bootstrapping a permutation test make sense?
Bootstrapping a permutation test doesn’t make sense. Next to that, determining a confidence interval around a test statistic doesn’t make sense either, as it is calculated based on your sample and not an estimate. You determine confidence intervals around estimates like means and the likes, but not around test statistics.