In Amplitude, just like in other data analysis tools, it’s important to understand the differences between the mean, average, and median, as each of these concepts provides different insights into your data. Here are the key differences explained:
- Mean: The mean is the average value of a dataset. It is calculated by adding up all the values in the dataset and then dividing the sum by the total number of values. It is sensitive to outliers or extreme values, as they can distort the mean. The mean is useful for understanding the average value of a metric and is widely used in statistics and data analysis.
- Weighted Average: The weighted average is similar to the mean, but it’s used when some values are more important or carry more weight than others. Each value in the dataset is multiplied by a corresponding weight before calculating the average. It’s useful when you want to give more importance to certain values in your data.
- Median: The median is the value that lies at the center of an ordered dataset. It is used to represent the central value of a dataset and is less sensitive to outliers compared to the mean. To calculate the median, you first need to sort the values in the dataset and then find the value in the middle. If the dataset has an even number of values, the average of the two middle values is taken.
In summary, the mean provides the arithmetic average of a dataset and is sensitive to extreme values, while the median represents the central value of a dataset and is less affected by outliers. The weighted average is used when some values carry more significance than others and is calculated by applying weights to each value. The choice between these three statistics depends on your analytical goals and the nature of your data.
