What is a benefit of using a moving average over raw data?

A moving average is a statistical technique commonly used in data analysis to smooth out fluctuations in data over time. By calculating the average of a set number of data points, it helps to highlight trends rather than the variability that can occur in raw data. This smoothing effect is particularly beneficial when analyzing data that exhibits periodic increases and decreases, as it reduces the noise created by these fluctuations, allowing analysts to focus on underlying patterns and long-term trends.

In contrast, other options relate to the functionality of the moving average but do not address its primary benefit. Adjusting for values that occur frequently may not be directly associated with the purpose of a moving average, as it does not inherently adjust for frequency distribution. Additionally, rather than displaying outliers more prominently, moving averages potentially obscure outliers because they average values over a set period, leading to a more generalized view of the data. This means that instead of highlighting anomalies, a moving average typically blends them into the overall trend. Thus, the correct choice emphasizes the key advantage of using a moving average: smoothing out periodic fluctuations.