# Point estimates and population parameters

## Learning outcomes

On watching this video, students should be able to:

1. Explain what is meant by statistical inference.
2. Define a point estimate and population parameter and list common types of point estimates and parameters
3. Identify point estimates and parameters when reading the scientific literature.

Some jargon please ensure you understand this fully:

Sample statistics or statistics are observable because we calculate them from the data (or sample) we collect. We use the "statistics" calculated from the sample to estimate the value of interest in the population. We call these sample statistics "point estimates" and this value of interest in the population, a population parameter. An example, would be to use the sample mean as a point estimate of the population mean, here the population mean is  the population parameter we are interested in finding out about.

A population parameter is assumed to be fixed or take only one value. Population parameters are unknown and almost always unknowable, because they "belong" to populations and we almost never observe whole populations. Common population parameters in a study are those used to describe the distributions of variables eg, the mean; but we can estimate any parameter we are interested in, for example the difference between two means or a difference in risk between two groups.

In a nut shell, a point estimate is a sample statistic obtained from the observed sample, and is used as our best guess of the unobserved population parameter.