a small response to When graphing data from a large population, it is preferable to utilize relative frequencies and smooth curves rather than exact frequencies as in a small sample. This is mostly because it is practically impossible to obtain an exact count of each individual score in a huge population. For example, determining how many adults in the United States have an IQ of exactly 110 would be prohibitively difficult for millions of people. Instead, we utilize relative frequencies to depict the proportion or likelihood of distinct scores, and we plot the data using a smooth curve, such as a normal distribution curve. We can better see the general form and structure of the data by using smooth curves. It makes it simpler to see the data's degree of dispersion, whether it is symmetrical or skewed, and where the majority of scores lie (the center). These curves are particularly useful for continuous variables that don't neatly fit into distinct categories, such as height, weight, or IQ. Histograms or bar graphs, on the other hand, perform better when we have a small sample, such as a group of 50 people, because we frequently know the precise frequencies for each score