Which sample is representative?
Representative samples are mainly used for surveys on attitudes, behavior and opinions of people for whom there is otherwise no precise statistical data (opinion polls, market research).
How big does a sample have to be to be representative?
A sample is representative with regard to an estimated size of the population if the corresponding sample estimate is true to expectations. unknown or uncorrected heterogeneity of the selection probabilities (e.g. people who travel are more difficult to reach.)
What do you have to consider with a random sample?
In an investigation, the aim should be that the sample does not contain any systematic deviations from the population (distortions). The easiest way to do this is to take the sample at random, but this is not always possible.
What kind of sample?
2 types of samples 2.1 quota sample. 2.2 Random sample. 2.3 Multi-stage selection process. 2.4 Lump selection. 2.5 Conscious selection process.
What is a homogeneous sample?
homogeneous sample variance exists if the variances determined for two or more samples do not differ significantly in size; the differences can be checked, for example, with the help of the F test or Bartlett test.
When is a variance homogeneous?
Uniformity of variance is given when the variance is roughly the same in all groups. Failure to do so would increase the likelihood of making a Type I mistake.
What does variances mean?
Uniformity of variance (also called homoscedasticity) is a prerequisite for the unpaired t-test. Given the homogeneity of variance, the variance in the two groups is (roughly) the same. A larger problem, however, is the lack of homogeneity of variance when calculating the standard error.
When is Anova significant?
p-value ≤ α: the differences between some of the means are statistically significant. If the p-value is less than or equal to the significance level, you reject the null hypothesis and conclude that not all population means are the same.
When to use two-factor analysis of variance?
The two-factor analysis of variance (ANOVA for short) tests independent samples to determine whether the means are different if there are more than two independent samples.
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