正态分布检验的3大步骤及结果处理 spss.docVIP

Statistics – Spring 2008 Lab #1 – Data Screening 7. Normality Below, I describe five steps for determining and dealing with normality. However, the bottom line is that almost no one checks their data for normality; instead they assume normality, and use the statistical tests that are based upon assumptions of normality that have more power (ability to find significant results in the data). First, what is normality? A normal distribution is a symmetric bell-shaped curve defined by two things: the mean (average) and variance (variability). Second, why is normality important? The central idea behind statistical inference is that as sample size increases, distributions will approximate normal. Most statistical tests rely upon the assumption that your data is “normal”. Tests that rely upon the assumption or normality are called parametric tests. If your data is not normal, then you would use statistical tests that do not rely upon the assumption of normality, call non-parametric tests. Non-parametric tests are less powerful than parametric tests, which means the non-parametric tests have less ability to detect real differences or variability in your data. In other words, you want to conduct parametric tests because you want to increase your chances of finding significant results. Third, how do you determine whether data are “normal”? There are three interrelated approaches to determine normality, and all three should be conducted. First, look at a histogram with the normal curve superimposed. A histogram provides useful graphical representation of the data. SPSS can also superimpose the theoretical “normal” distribution onto the histogram of your data so that you can compare your data to the normal curve. To obtain a histogram with the superimposed normal curve: 1. Select Analyze -- Descriptive Statistics -- Frequencies. 2. Move all variables into the “Variable(s)” window. 3. Click “Charts”, and click “Histogram, with normal curve”. 4. Click OK. Output below is f