Sampling

# Parameter estimation. Point estimation

Inferential statistics try to infer information about a population through random samples.

Inside inferential statistics we find inductive statistics, which estimate the population parameters through the sample ones. This can be done by using intervals or points.

**Point estimation** consists in estimating the unknown population parameter by a unique value. This estimation is more precise but less reliable than the interval one.

The statistic which we use to estimate it is called **point estimate**.

Point estimates can be:

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**Unbiased point estimate**: if the mean of the sample distribution of a statistic equals the corresponding population parameter. They are: •Sample mean is the unbiased estimate of the population mean

•Sample proportion is the unbiased estimate of the population proportion

•Difference between sample means is the unbiased estimate of the difference between population means

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**Biased point estimate**: if the mean of the sample distribution of a statistic doesn’t equal the corresponding population parameterWe must use an unbiased point estimate, the most efficient one. That is, the one whose sample distribution has less dispersion.

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