When two distributions with different sample sizes are being compared, relative frequency histograms are preferable to frequency histograms.
The quantity of observations that make up a distribution is its sample size.
A relative frequency histogram would be the ideal technique to compare two data sets when the sample sizes are different since they show a probability distribution in graphical form.
The heights of the histogram can therefore be interpreted as probabilities (results to occur within a given population)
A Frequency Histogram would display results for data set B that are a thousand times greater than those for data set A, but a Relative Frequency Histogram would display the results on the same scale, for instance, if distribution A has a sample size of n(A) = 52 and distribution B has a sample size of n(B) = 52000.
Utilizing a Relative Frequency Histogram is the most effective method for comparing two distributions with differing sample sizes.
Therefore,
When two distributions with different sample sizes are being compared, relative frequency histograms are preferable to frequency histograms.
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