In the world of data, where numbers reign supreme, understanding the difference between a **parameter** and a **statistic** is crucial. Here's the key difference:

**Parameter:**

- Represents a characteristic of the
**entire population**you're interested in. - It's a
**fixed, unknown value**you're trying to estimate. - Think of it as the
**true mean, proportion, or other measure**of the entire population (like the average height of all humans). - It's usually denoted by Greek letters (e.g., mu for population mean, sigma for population standard deviation).

**Statistic:**

- Represents a characteristic of a
**sample**drawn from the population. - It's a
**calculated value**based on the data you actually have. - Think of it as an
**estimate**of the true parameter based on a smaller group (like the average height of your classmates). - It's usually denoted by Roman letters (e.g., x-bar for sample mean, s for sample standard deviation).

Here's an analogy:

- Imagine you want to know the
**average weight of all elephants**on Earth (parameter). You can't weigh every elephant, so you take a**sample of 100 elephants**and calculate their average weight (statistic). This statistic**estimates**the true average weight, but it might not be exactly the same due to sampling variability.

Here are some additional key points:

- You can never
**directly measure**a parameter, but you can**estimate it**using statistics. - The more representative your sample is of the population, the more likely your statistic is to be close to the true parameter.
- Different statistics can be used to estimate different parameters.

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