![]() ![]() Usually, a significance level (denoted as α or alpha) of 0.05 works well. To determine whether the difference between the population variance or the population standard deviation and the hypothesized value is statistically significant, compare the p-value to the significance level. Because there is no test statistic for the Bonnet method, Minitab uses the rejection regions that are defined by the confidence limits to calculate a p-value. The test statistic is used to calculate the p-value. If it is not, you fail to reject the null hypothesis. If the test statistic is greater than the value, you reject the null hypothesis. For a one sided test of greater than, the critical value is.If the test statistic is less than the value, you reject the null hypothesis. For a one sided test with an alternative hypothesis of less than, the critical value is.If it is in between, you fail to reject the null hypothesis. If the test statistic is less than the first value or greater than the second value, you reject the null hypothesis. For a two sided test, the critical values are and.For more information, go to Using the inverse cumulative distribution function (ICDF) and click "Use the ICDF to calculate critical values". You can calculate the critical value in Minitab or find the critical value from a chi-square table in most statistics books. To determine whether to reject the null hypothesis, compare the test statistic to your critical values. For more information, go to Ways to get a more precise confidence interval. If the interval is too wide to be useful, consider increasing your sample size. Use your specialized knowledge to determine whether the confidence interval includes values that have practical significance for your situation. The confidence interval helps you assess the practical significance of your results. A lower bound defines a value that the population standard deviation or population variance is likely to be greater than. For example, a 95% confidence level indicates that if you take 100 random samples from the population, you could expect approximately 95 of the samples to produce intervals that contain the population standard deviation or the population variance.Īn upper bound defines a value that the population standard deviation or population variance is likely to be less than. The percentage of these confidence intervals or bounds that contain the standard deviation or the variance is the confidence level of the interval. But, if you repeated your sample many times, a certain percentage of the resulting confidence intervals or bounds would contain the unknown population standard deviation or population variance. Because samples are random, two samples from a population are unlikely to yield identical confidence intervals. ![]() The confidence interval provides a range of likely values for the population standard deviation or the population variance.
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