The p-value is greater than the significance level of 0.05. You cannot conclude that the data do not follow a normal distribution. Because the p-value is 0.4631, which is greater than the significance level of 0.05, the decision is to fail to reject the null hypothesis. For example, in the following results, the null hypothesis states that the data follow a normal distribution. For some 3-parameter distributions, the p-value is impossible to calculate and is represented by asterisks. A low p-value (e.g., < 0.05) indicates that the data don’t follow that distribution. It’s generally valid to compare p-values between distributions and go with the highest. "The p is low so the null must go," as they say. The null hypothesis for the normality test is that it is normally distributed our alternative that it is not. But let’s see what the humble probability plot can tell us. Remember to keep your eyes on the histogram and the normal probability plot in conjunction with the Anderson-Darling test before making any decision. normal probability plot - minitab ad valueĪ lower p-value than the significance level (normally 0.05) indicates a lack of normality in the data (regardless of the AD value). The points at the upper or lower extreme of the line, or which are distant from this line, represent suspected values or outliers. The points located along the probability plot line represent “normal,” common, random variations. Therefore, the scientist fails to reject the null hypothesis that the data follow a normal distribution. The data points are relatively close to the fitted normal distribution line (the middle solid line of the graph). Larger values for the Anderson-Darling statistic indicate that the data do not follow a lognormal distribution. Smaller p-values provide stronger evidence against the null hypothesis. The p-value is a probability that measures the evidence against the null hypothesis. Minitab uses the Anderson-Darling statistic to calculate the p-value. The normal probability plot of the residuals is approximately linear supporting the condition that the error terms are normally distributed. If your data are perfectly normal, the data points on the probability plot form a straight line. To visualize the fit of the normal distribution, examine the probability plot and assess how closely the data points follow the fitted distribution line. Use a probability plot to visualize how well your data fit the normal distribution. The normal distribution appears to be a good fit to the data. In this probability plot, the data form an approximately straight line along the line. In Minitab, hold your pointer over the fitted distribution line to see a chart of percentiles and values. Some objective measure of the straightness of a probability plot would be helpful, especially for students just beginning their statistical education. One problem confronting persons inexperienced with probability plots is that considerable practice is necessary before one can learn to judge them with any degree of confidence. Normal probability plots are often used as an informal means of assessing the non-normality of a set of data. Normal Probability Plots and Tests for Normality. For a normal distribution with a mean and standard deviation equal to the data, you would expect 5% of the population to have a pulse rate of 54.76 or less. For example, the following graph shows normal distributions with means of 1 and −1 and with standard deviations of 1 and 2.įor example, the following probability plot shows the pulse rates of test subjects as they walked on a treadmill. The standard deviation defines the spread of a normal distribution. The mean defines the peak or center of a normal distribution. When you fit a normal distribution, Minitab estimates these parameters from your sample. For example, the following probability plot shows the pulse rates of test subjects as they walked on a treadmill.Ī normal distribution is defined by two parameters: the mean and the standard deviation. In Minitab, hold your pointer over the fitted distribution line to see a table of percentiles and values. Normal probability plot - minitab interpretation
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