Hypothesis testing statistics
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Hypothesis testing
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Descriptions, definitions, synonyms, organizer terms, types of
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Application
Test 1: Hypothesis Testing From: delta
A question is translated into the hypothesis where a given statement is made to be tested. The results of testing show acceptance or rejection of the hypothesis.
The first hypothesis in a test is null hypothesis (H0)that is a true/false statement. Then there is an alternative hypothesis (HA).
The steps are to test that true/false statement for truth. If it isn't true, then the alternative is.
The result of hypothesis testing is to reject the true/false statement or not. There will either be evidence favoring the alternative hypothesis or not. Obviously, if there is enough evidence, the alternative is true.
In summary, the steps are to define the true/false null hypothesis and then the alternative hypotheis. Then both are tested. Which is true?
Some Issues:
- significant differences and rejecting the null hypothesis
- there are almost always differences (if you look closely enough), but should you pay attention to the differences you find?
- significance level (aka alpha; aka p value)
- t-tests: comparing two numeric data sets
- ANOVA (Analysis of variance): comparing more than two numeric data sets
- ANCOVA (Analysis of covariance): comparing data sets while controlling for variables not of primary interest
- chi-square (for categorical data)
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Evidence of effectiveness
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Critics and their rationale
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Alternative explanations due to Diversity considerations
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Signed “life experiences”, testimonies and stories
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