Any small deviation from normality can cause the F-test to be inaccurate, even with large samples. The F-test is accurate only for normally distributed data.For extremely skewed and heavy tailed distributions, Levene's method tends to be more reliable than Bonett's method. Levene's test is also accurate with any continuous distribution.Bonett's test is usually more reliable than Levene's test. Bonett's test is accurate for any continuous distribution and does not require that the data are normal.Choose a test on the properties of the distribution of the data, as follows: If you use the test for the normal distribution, Minitab displays the p-value for the F-test. For more information, go to Power andīy default, the 2 variances test displays the p-values for Levene's method and Bonett's method. You should make sure that your test has enough power to detect a difference that is practically significant. You do not have enough evidence to conclude that the ratio of the population standard deviations or variances is statistically significant. P-value > α: The ratio of the standard deviations or variances is not statistically significant (Fail to reject H 0) If the p-value is greater than the significance level, the decision is to fail to reject the null hypothesis. For more information, go to Statistical and practical significance. Use your specialized knowledge to determine whether the difference is practically significant. If you did not specify a hypothesized ratio, Minitab tests whether no difference between the standard deviations or variances ( Hypothesized ratio = 1) exists. You can conclude that the ratio of the population standard deviations or variances is not equal to the hypothesized ratio. P-value ≤ α: The ratio of the standard deviations or variances is statistically significant (Reject H 0) If the p-value is less than or equal to the significance level, the decision is to reject the null hypothesis. A significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference. ![]() Usually, a significance level (denoted as α or alpha) of 0.05 works well. To determine whether the difference between the population standard deviations or variances is statistically significant, compare the p-value to the significance level. The summary plot shows the confidence interval for the ratio and the confidence interval for either the standard deviations or variances. For more information, go to Should I use Bonett's method or Levene's method for 2 Variances?. Any small deviation from normality can greatly affect the F-test results. Use the F-test only if you are certain that the data follow a normal distribution. However, for extremely skewed and heavy tailed distributions, Levene's method is usually more reliable than Bonett's method. Bonett's method is usually more reliable than Levene's method. For more information, go to Ways to get a more precise confidence interval.īy default, the 2 variances test displays the results for Levene's method and Bonett's method. ![]() 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. 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 ratio. The confidence interval provides a range of likely values for the ratio between two population variances or standard deviations. To better estimate the ratio, use the confidence interval. Because the estimated ratio is based on sample data and not on the entire population, it is unlikely that the sample ratio equals the population ratio. The estimated ratio of standard deviations and variances of your sample data is an estimate of the ratio in population standard deviations and variances. First, consider the ratio in the sample variances or the sample standard deviations, and then examine the confidence interval.
0 Comments
Leave a Reply. |