Explanation

When your data contain extreme values, are heavily skewed, or are measured on an ordinal scale, the median often gives a more reliable picture of the typical observation than the mean. The median is the 50th percentile, so it is unaffected by the magnitude of values far from the centre. This makes it especially useful in income distributions, real‑estate prices, or any scenario where a few large observations can dominate the arithmetic average. You should also prefer the median when you need a measure that is easy to interpret for non‑technical stakeholders, because it directly answers “what is the middle value?”

Naive Response

I think the median is better when there are outliers because the mean gets pulled away. So if the data are skewed, just use the median. It’s simpler and you don’t have to worry about the math.

Good Response

When the distribution is right‑skewed—such as household incomes, property values, or transaction amounts—the mean is pulled toward the long tail, while the median stays near the bulk of the data. By reporting the median, you convey the typical experience of the majority rather than the average that is inflated by a few high‑value cases. This is also true for left‑skewed data, where low outliers drag the mean down.

In datasets with genuine outliers—measurement errors, fraud, or rare extreme events—the median is robust because it ignores the actual values of those points. For example, if one product’s price is mistakenly entered as $10,000 instead of $100, the mean price will be dramatically overstated, but the median will remain close to the true central price.

Ordinal data, such as Likert‑scale survey responses (e.g., “strongly disagree” to “strongly agree”), do not have a meaningful arithmetic average. The median can be computed by ordering the categories, giving a sensible central tendency without assuming equal intervals between categories.

Finally, when communicating results to business stakeholders, the median is often more intuitive: “half of our customers spend less than $X” is a clear statement, whereas “the average spend is $X” can be misleading if the spend distribution is uneven.

Why the Good Response is Better

It identifies specific data scenarios (right‑skewed, left‑skewed, outliers, ordinal) rather than a generic statement.
It connects the statistical property of the median (robustness) to concrete business examples, making the advice actionable.
The structure uses multiple short paragraphs, each focusing on a distinct reason, which improves clarity and readability.

7. Median vs Mean

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