# 6-Nov-2008 Normal quantile & probability plots

In a previous post, , we explained the tests provided in Analyse-it to determine if a sample has normal distribution. In that post, we mentioned that although hypothesis tests are useful you should not solely rely on them. You should always look at the histogram and, maybe more importantly, the *normal plot*.

The beauty of the normal plot is that it is designed specifically for judging normality. The plot is very easy to interpret and lets you see where the sample deviates from normality.

As an example, let’s look at the distribution of systolic blood pressure, for a random group of healthy patients. Analyse-it creates the histogram (left) and normal plot (right) below:

Looking at the histogram, you can see the sample is approximately normally distributed. The bar heights for 120-122 and 122-124 make the distribution look slightly skewed, so it’s not perfectly clear.

The normal plot is clearer. It shows the *observations* on the X axis plotted against the *expected normal score* (Z-score) on the Y axis. It’s not necessary to understand what an *expected normal score *is, nor how it’s calculated, to interpret the plot. All you need to do is check is that the points roughly follow the red-line. The red-line shows the ideal normal distribution with mean and standard-deviation of the sample. If the points roughly follow the line – as they do in this case – the sample has normal distribution.