The way that we specify the bins will have a major effect on how the histogram can be interpreted, as will be seen below. Instead, setting up the bins is a separate decision that we have to make when constructing a histogram. Information about the number of bins and their boundaries for tallying up the data points is not inherent to the data itself. For example, even if the score on a test might take only integer values between 0 and 100, a same-sized gap has the same meaning regardless of where we are on the scale: the difference between 60 and 65 is the same 5-point size as the difference between 90 to 95. This means that the differences between values are consistent regardless of their absolute values. In order to use a histogram, we simply require a variable that takes continuous numeric values. You can see roughly where the peaks of the distribution are, whether the distribution is skewed or symmetric, and if there are any outliers.

Histograms are good for showing general distributional features of dataset variables. If we only looked at numeric statistics like mean and standard deviation, we might miss the fact that there were these two peaks that contributed to the overall statistics. There’s also a smaller hill whose peak (mode) at 13-14 hour range. We can see that the largest frequency of responses were in the 2-3 hour range, with a longer tail to the right than to the left. Each bar covers one hour of time, and the height indicates the number of tickets in each time range. The histogram above shows a frequency distribution for time to response for tickets sent into a fictional support system. Each bar typically covers a range of numeric values called a bin or class a bar’s height indicates the frequency of data points with a value within the corresponding bin. A histogram is a chart that plots the distribution of a numeric variable’s values as a series of bars.

Relative frequency histogram excel