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Expected frequencies are used in a chi-squared "goodness-of-fit" test. there is a hypothesis that is being tested and, under that hypothesis, the random variable would have a certain distribution. The expected frequency for a "cell" is the number of observations that you would expect to find in that cell if the hypothesis were true.
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If the chi-square is very large what does it mean?
The null hypothesis in a chi-square goodness-of-fit test states that the sample of observed frequencies supports the claim about the expected frequencies. So the bigger the the calculated chi-square value is, the more likely the sample does not conform the expected frequencies, and therefore you would reject the null hypothesis. So the short answer is, REJECT!
How is the chi square independence test similar to the goodness of fit test how is it different?
With either test, you have a number of categories and for each you have an expected number of observations. The expected number is based either on the variable being independent of some other variable, or determined by some know (or hypothesised) distribution. You will also have a number of observations of the variable for each category. The test statistic is based on the observed and expected frequencies and has a chi-squared distribution. The tests require the observations to come from independent, identically distributed variables.
What is the purpose of Chi Square Analysis?
Chi-square is mainly used for a goodness of fit test. This is a test designed to assess how well a set of observations agree with what might be expected from some hypothesised distribution.
How do pronounce chi-square test?
The chi-square test is pronounced "keye-skwair" test.
Is the chi square of independence test a test a test of significance?
Yes.
