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Statistics is primarily used either to make predictions based on the data available or to make conclusions about a population of interest when only sample data is available.

In both cases statistics tries to make sense of the uncertainty in the available data.

When making predictions statisticians determine if the difference in the data points are due to chance or if there is a systematic relationship.

The more the systematic relationship that is observed the better the prediction a statistician can make.

The more random error that is observed the more uncertain the prediction.

Statisticians can provide a measure of the uncertainty to the prediction.

When making inference about a population, the statistician is trying to estimate how good a summary statistic of a sample really is at estimating a population statistic.

For example, a statistician may be asked to estimate the proportion of women who smoke in the US. This is a population statistic.

The only data however may be a random sample of 1000 women.

By estimating the proportion of women who smoke in the random sample of 1000, a statistician can determine how likely the sample proportion is close to the population proportion.

A statistician would report the sample proportion and an interval around that sample proportion. The interval would indicate with 95% or 99% certainty that the population proportion is within that interval, assuming the sample is really random.

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