If you're reading this to complete some assignment, you're probably asked to report some descriptive statistics for some variables. Descriptive Statistics - Skewness & Kurtosis
#SPSS 22 TRIAL TRIAL#
An exception is trial 4 (shown below) which looks plausible -even reasonably normally distributed. Some data seem corrupted and had better not be seriously analyzed. Note that some of the 5 histograms look messed up. frequencies r01 to r05 /format notable /histogram normal. *Quick histograms with normal curves as data check. Easier -but slower- methods are covered in Creating Histograms in SPSS. I prefer doing so from the short syntax below. Since our reaction times in milliseconds are quantitative variables, we'll run some quick histograms over them. I recommend you always thoroughly inspect all variables you'd like to analyze. We'll only use the first five trials in variables r01 through r05. Their reaction times are in speedtasks.sav, partly shown below. If this probability is (very) small -but we found our data anyway- then the null hypothesis was probably wrong.Ī sample of N = 236 people completed a number of speedtasks. Why? Well, p is basically the probability of finding our data if the null hypothesis is true. So in this case we conclude that our variable is not normally distributed. The null hypothesis for the Shapiro-Wilk test is that a variable is normally distributed in some population.Ī different way to say the same is that a variable’s values are a simple random sample from a normal distribution. It does so under the assumption that the population distribution is exactly normal: the null hypothesis.
![spss 22 trial spss 22 trial](https://spssdownload.com/wp-content/uploads/2020/09/spss-software-20-FREE-DONLOAD-FULL-VERSION.jpg)
It then computes which percentage of our sample overlaps with it: a similarity percentage.įinally, the Shapiro-Wilk test computes the probability of finding this observed -or a smaller- similarity percentage. However, a simpler -but not technically correct- explanation is this: the Shapiro-Wilk test first quantifies the similarity between the observed and normal distributions as a single number: it superimposes a normal curve over the observed distribution as shown below. How Does the Shapiro-Wilk Test Work?Ī technically correct explanation is given on this Wikipedia page. The Shapiro-Wilk test answers precisely that. How likely is the observed distribution if the reaction timesĪre exactly normally distributed in the entire population?
![spss 22 trial spss 22 trial](https://i.pinimg.com/1200x/37/73/f1/3773f11ac44b5fa7b73532bc742b924e.jpg)
However, sample outcomes usually differ from their population counterparts. Other than that, it looks reasonably -but not exactly- normal. This frequency distribution seems somewhat bimodal. A histogram of the results is shown below. He draws a random sample of N = 233 people and measures their reaction times. Others disagree.Īs an example of a Shapiro-Wilk test, let's say a scientist claims that the reaction times of all people -a population- on some task are normally distributed.
![spss 22 trial spss 22 trial](https://els-jbs-prod-cdn.jbs.elsevierhealth.com/cms/attachment/9582b9e2-75c4-42c9-b0be-3327378977c3/gr1.gif)
Some statisticians claim the latter is worse due to its lower statistical power. Like so, the Shapiro-Wilk serves the exact same purpose as the Kolmogorov-Smirnov test. Is normally distributed in some population. The Shapiro-Wilk test examines if a variable