# How do you evaluate the p value of the tide?

## How do you evaluate the p value of the tide?

As a rule, the maximum tolerable probability of error, the so-called significance level (), is set at 5%. The method that leads to the confirmation of the hypothesis (with the maximum probability of error) or its non-confirmation based on the results of the sample is the statistical test.

## When do you make Bonferroni corrections?

The Bonferroni correction is always used when you carry out several related tests. For example, imagine you have three groups whose means you want to compare.

## When is a result significantly worth p?

The result of the test gives the p-value, the probability of error. If this p-value is below α = 5%, the result is considered significant.

## How do you calculate statistical significance?

How to Calculate Statistical Significance Determine what you want to test. First, you need to determine what exactly you want to test. Record your data. Now that you have determined what you want to test, enter your data. Calculate your chi-square results.

## When do you use the T test?

The t-test enables you to test hypotheses about the mean value (s) of the population based on the realizations of your sample (s) if you can assume a normal distribution for the population but do not know the variance of the population.

## When T test and when F test?

The F-test checks whether the variances of two samples are the same in the statistical sense, i.e. homogeneous, and consequently come from the same population. Uniformity of variance is, for example, a prerequisite for the t-test for independent samples and for analysis of variance (ANOVA).

## When T test for independent samples?

A t-test for independent samples can be used when the means of two different samples are to be compared with one another and the differences are to be tested for significance.

## When T test for dependent samples?

The t-test for dependent samples tests whether the means of two dependent samples are different. We speak of “dependent samples” or “connected samples” when a measured value in one sample and a certain measured value in another sample influence each other.

## When is a sample connected?

Linked samples are data measured on the same cases / patients. For example, if a laboratory parameter is measured on all patients before and after treatment, then you have two samples: one with measurements before treatment and. one with measurements after treatment.

## Is my sample dependent or independent?

If the values in one sample influence the values in the other sample, the samples are interdependent. If the values in one sample do not contain information about the values in the other sample, the samples are independent of each other.

## Which test for data that are not normally distributed?

If they are normally distributed, I can use a parametric test. If they are not, a non-parametric one must be used. If you were to compare two groups with a normal distribution, this would be the famous t-test. If the distribution is not normal, the Mann-Whitney-U test.

## How do I know if something is normally distributed?

The Shapiro-Wilk test is a statistical significance test that tests the hypothesis that the underlying population of a sample is normally distributed. The test was developed by Samuel Shapiro and Martin Wilk and first introduced in 1965.

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