Descriptive Statistics

Descriptive statistics is the art of summarizing and simplifying data to make sense of the numbers. It allows us to paint a clearer picture of what’s happening in a dataset without getting lost in the details. One key area is measures of central tendency, which include the mean, median, and mode — giving us an idea of where most values in a dataset cluster. But numbers don’t always tell the full story, which is why we also look at dispersion — like range, variance, and standard deviation — to understand how spread out or tightly packed the data points are.

Next, we dive into skewness, which reveals if the data is leaning more toward one side (left or right), while kurtosis helps us understand how heavy the tails of the data distribution are — whether there are extreme outliers or not. Moments give us deeper insights into the shape of the distribution, providing even more nuance to our data understanding. Correlation explores the relationships between variables, helping us see if they move together or in opposite directions. Finally, index numbers allow us to track changes in data over time, often used to measure economic indicators like inflation. In short, descriptive statistics is essential for uncovering the story behind the numbers and transforming raw data into meaningful insights.