What is the Difference Between RMS and Average?

The main difference between RMS (Root Mean Square) and average values lies in the way they are calculated and their applications. Here are the key differences:

1. Calculation: Average value is the sum of all values in a dataset divided by the total number of values. In contrast, RMS value is calculated by squaring each value in the dataset, taking the average (mean) of the squared values, and then taking the square root of the result.
2. Representation: Average can be expressed in various ways, such as mean, median, or mode. In RMS, the average is expressed as an arithmetic mean.
3. Applications: Average is used to obtain the central tendency of a given dataset, while RMS is used when random values are present in the dataset and has significant relevance in electrical engineering.
4. Usage: Average values are broadly used in various scientific and engineering fields, while RMS values are particularly used in electrical engineering to represent the effective equivalent DC voltage in AC circuits.

In summary, RMS values are more mathematically complex than average values and are commonly used in electrical engineering to represent the effective DC voltage in AC circuits. Average values, on the other hand, are used to obtain the central tendency of a dataset and can be expressed in various ways, such as mean, median, or mode.

Comparative Table: RMS vs Average

The main difference between the root mean square (RMS) and the average of a set of values lies in the way they represent the central tendency of the data. Here's a comparison between the two:

In summary, RMS is often used when the magnitude of values is important, while the average is a more general-purpose measure of central tendency. RMS is calculated as the square root of the mean of the squares of the values, whereas the average is calculated by adding up all the values and dividing the result by the number of values.