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- A confidence interval is generally created when statistical tests fail to reject the null hypothesis – that is, when results are not statistically significant.
- Chi-square tests rarely have type I errors
- Chi-square tests are more likely to have type II (falsely rejecting the null hypothesis) errors than parametric tests.
- For a one sample confidence interval, the interval is calculated around the calculated sample mean (m).
- Compared to the ANOVA test, Chi-Square procedures are not powerful (able to detect small differences).
- While rejecting the null hypothesis for the goodness of fit test means distributions differ, rejecting the null for the test of independence means the variables interact.
- The distribution for the goodness of fit test equals k-1, where k equals the number of categories.
- The Chi-square test is very sensitive to small differences in frequency differences.
- The goodness of fit test can be used for a single or multiple set (rows) of data, such as comparing male and female age distributions with an expected distribution at the same time.
- The probability that the actual population mean will be outside of a 98% confidence interval is