What are the key differences between measures of central tendency?

1 Mean Matters

The mean, often referred to as the average, is calculated by adding all the values in a data set and dividing by the number of values. In BI, the mean is useful for identifying the overall trend of data points in a continuous dataset. However, it's sensitive to outliers or extreme values, which can skew the results. For example, if you're analyzing sales data and a single transaction is significantly higher than the rest, the mean will be pulled towards that high value, potentially giving a misleading impression of typical sales figures.

2 Median Insights

The median is the middle value of a data set when arranged in ascending or descending order. If there is an even number of observations, the median is the average of the two middle numbers. This measure is less affected by outliers and skewed data, making it more reliable in those situations. For instance, in BI, when analyzing income data that includes a few extremely high earners, the median will give a better sense of what a "typical" individual earns than the mean.

3 Mode Relevance

The mode is the value that appears most frequently in a data set. There can be one mode, more than one (bimodal or multimodal), or no mode at all if no number repeats. In BI, mode is particularly useful for categorical data where you want to know the most common category or response. For example, if you're analyzing the most preferred product features among customers, the mode can quickly indicate which feature is most popular.

4 Range and IQR

While not measures of central tendency, understanding range and interquartile range (IQR) is important as they provide context. The range is the difference between the highest and lowest values in a dataset. The IQR measures the spread of the middle 50% of data points. Both are valuable in BI for understanding the variability around measures of central tendency, which can influence how you interpret the mean, median, and mode.

5 Skewed Distributions

Skewed distributions occur when data points cluster asymmetrically around a central value, resulting in a long tail on one side of the distribution. In positively skewed distributions, the mean is greater than the median, which is greater than the mode. Conversely, in negatively skewed distributions, the mode is greater than the median, which is greater than the mean. Recognizing skewness is important in BI as it can affect the choice of measure for central tendency to best represent your data.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?