Hypothesis Testing Help
Hypothesis Testing is the corner stone of Inferential Statistics, and they provide the corner stone for making statements about population parameters by using sample data. If the sample statistics are too unusual under the assumption that the null hypothesis (the hypothesis of no effect) is true, then the null hypothesis is rejected
In order to conduct inferential statistics procedures, it is necessary that certain assumptions are met. These assumptions are typically the fulfillment of the normality assumption (the samples come from normally distributed populations) and equality of variances. When any of the assumptions is not met, then alternative procedures need to be conducted, namely, Non-Parametric testing.
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We provide you help with the following:
- Z-test and T-test for one population mean
- Z-test for one population proportion
- Z-test and T-test for two population mean
- Z-test for two population proportion
- Sign Test
- Wilcoxon Signed Ranks
- Wilcoxon Rank Sum
- Chi-Square tests
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