![]() Generally we compare the p-value with a user defined level of significance denoted by alpha or a and make a decision as: When looking at the p-values, there are different guidelines on when to accept or reject the null hypothesis, (recall from our earlier.discussion that the null hypothesis states that the sample values are normally distributed). Let us now talk about how to interpret this result. The function shapiro.test(x) returns the name of data, W and p-value. ![]() You will see the following output: Shapiro-Wilk normality test This W is also referred to as the Shapiro-Wilk statistic W (W for Wilk) and its range is 0 shapiro.test(x) HA : the sample is not normally distributed Specify the null hypothesis and the alternative hypothesis as: To avert this problem, there is a statistical test by the name of Shapiro-Wilk Test that gives us an idea whether a given sample is normally distributed or not. However, this may not always be true leading to incorrect results. When the distribution of a real valued continuous random variable is unknown, it is convenient to assume that it is normally distributed. This is in agreement with the P(x) expression we saw earlier. The histogram shows us that the values are symmetric about the mean value zero, more values occur close to the mean and as we move away from the mean, the number of values becomes less and less.
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