Q.Out of Mean and Median, which one is more sensitive to outliers in data?

Descriptive Statistics: Making Sense of the World Through Numbers

You walk into a classroom of thirty students. Without thinking, you notice things: most people are wearing blue jeans, a few are talking loudly, the room feels warm. That's your brain doing descriptive work — noticing patterns, picking out what's common and what's unusual.

Descriptive statistics is exactly that instinct, but made precise and trustworthy. It is the art and science of summarising and organising data so that a large, messy collection of facts becomes something you can understand and talk about.

The Core Idea

When you have a pile of information — say, the marks of 200 students in an exam, or the monthly sales of a shop over three years — you cannot simply stare at the raw list and grasp what is happening. The numbers are too many, too scattered. Descriptive statistics gives you tools to compress that information without losing its meaning.

It does two things:

• It organises the data into a sensible form (tables, charts, frequency distributions).
• It summarises the data using a few key measures (averages, spreads, positions).

The key word is describe. You are not making guesses about a larger population, not testing a hypothesis, not predicting the future. You are simply saying: this is what the data we have looks like.

Why It Matters for Commerce and Humanities

In commerce, you might look at the sales figures of a product across different cities. Descriptive statistics tells you: which city sells the most (the average), whether sales are steady or wildly fluctuating (the spread), and whether most cities cluster around a typical value or are all over the place (the distribution shape).

In humanities, you might study survey responses about political opinions. Descriptive statistics tells you: what the most common opinion is, how divided the group is, and whether a few extreme views are pulling the overall picture one way.

Without these tools, you are drowning in raw numbers. With them, you can speak clearly about what the data says.

The Two Main Families of Descriptive Measures

Measures of Central Tendency

These answer the question: What is a typical value?

• The mean is the arithmetic average — add everything up and divide by how many items you have. It is the most familiar, but it can be pulled off-centre by one very high or very low value.
• The median is the middle value when you arrange all numbers in order. It is not affected by extreme values, so it often gives a truer picture of "typical" when data is skewed.
• The mode is the value that appears most frequently. It is the only measure that works for non-numerical data — like "blue jeans" being the most common clothing item in that classroom.

Measures of Dispersion

These answer the question: How spread out are the values?

• The range is the simplest — the difference between the highest and lowest value. It tells you the full span, but it can be misleading if there is one extreme outlier.
• The interquartile range (IQR) is the range of the middle 50% of the data. It ignores the extremes and tells you where the bulk of the data lies.
• The standard deviation is the most powerful measure. It tells you, on average, how far each value is from the mean. A small standard deviation means the data is tightly clustered; a large one means it is scattered widely.