Relative Frequency is useful when comparing the absolute

We can start by defining what relative frequency and absolute frequency mean in the context of data analysis.
- Absolute Frequency: refers to the number of times a particular value or category appears in a dataset.
- Relative Frequency: refers to the proportion or percentage of times a particular value or category appears in a dataset.
Now, to answer the question, we can say that the statement is:
$\mathbf{\text{True}}$
Relative frequency is indeed useful when comparing absolute frequency results within a dataset.
The reason for this is that absolute frequency values can be misleading if the dataset has a large variation in size, as larger datasets will naturally have larger absolute frequency values. On the other hand, relative frequency values can provide a more accurate representation of the dataset by providing proportions or percentages that can be compared across different dataset sizes.
For example, let's say we have two datasets:
- Dataset A has 100 observations, and 20 of them are red.
- Dataset B has 1000 observations, and 200 of them are red.
If we compare the absolute frequency of red in both datasets, we get:
- Dataset A: 20
- Dataset B: 200
However, if we compare the relative frequency of red in both datasets, we get:
- Dataset A: 20% (20/100)
- Dataset B: 20% (200/1000)
Now we can see that the proportion of red is the same in both datasets, even though the absolute frequency values are different.
Therefore, we can conclude that relative frequency is useful when comparing the absolute frequency results within a dataset.