Deviationsannahmen

Deviationsannahmen, also known as assumptions of deviation, are a statistical concept used in hypothesis testing and regression analysis. They are a set of conditions that must be met for the statistical tests and models to be valid and reliable. These assumptions ensure that the results obtained from the analysis are accurate and can be generalized to the population from which the sample was drawn.

In hypothesis testing, the deviationsannahmen typically include:

1. Independence: The observations in the sample must be independent of each other. This means that the value of one observation should not influence the value of another.

2. Normality: The data should follow a normal distribution. This assumption is crucial for tests like the t-test and ANOVA.

3. Homogeneity of variance: The variances of the populations from which the samples are drawn should be equal. This assumption is important for tests that compare means between groups.

In regression analysis, the deviationsannahmen include:

1. Linearity: The relationship between the independent and dependent variables should be linear.

2. Independence: The residuals (the differences between the observed and predicted values) should be independent.

3. Homoscedasticity: The residuals should have constant variance at every level of the independent variable.

4. Normality: The residuals should follow a normal distribution.

If these assumptions are not met, the results of the statistical tests and models may be biased or inaccurate. In such cases, alternative methods or transformations may be used to meet the assumptions or to adjust for violations.