These are proportionally converted to values in the range $0$ to $1$. First each data value is ranked from $1$ through $n$, the amount of data (shown in the Count field in cell F2). The comparison values on the vertical axis are computed in two steps. This is perfectly fine for applying a t-test. Thus we see at a glance that these data are very close to Normally distributed but perhaps have a slightly "light" right tail. In this example the points are remarkably close to the reference line the largest departure occurs at the highest value, which is about $1.5$ units to the left of the line. When the points line up along the diagonal, they are close to Normal horizontal departures (along the data axis) indicate departures from normality.
This plot is a scatterplot comparing the data to values that would be attained by numbers drawn independently from a standard Normal distribution. The rest is all calculation, although you can control the "hinge rank" value used to fit a reference line to the plot. From it you can see much more detail than appears in other graphical representations, especially histograms, and with a little practice you can even learn to determine ways to re-express your data to make them closer to Normal in situations where that is warranted.ĭata are in column A (and named Data).
A graph of the results is called a normal probability plot (or sometimes a P-P plot). This can be done systematically, comprehensively, and with relatively simple calculations.