What graphical representation is used to assess normality in data?

The normal probability plot is specifically designed to assess whether a set of data approximates a normal distribution. In a normal probability plot, the data points are plotted against the expected z-scores if they were normally distributed. If the data is normally distributed, the points will roughly form a straight line. Deviations from this line indicate departures from normality, such as skewness or kurtosis.

While the other graphical representations serve different purposes, they do not directly provide the same assessment of normality. A box plot offers insight into the distribution, such as median and potential outliers but does not give a direct comparison to a normal distribution. A histogram displays the frequency of data in intervals, which can suggest normality at a glance, but it lacks the precise quantification that a normal probability plot offers. A scatter plot is typically used to investigate the relationship between two continuous variables, rather than to evaluate the distribution of a single variable. Therefore, the normal probability plot is the most suitable option for assessing normality in data.