Population & Sample

A population is the entire group that you want to draw conclusions about.
A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population.
In research, a population doesn’t always refer to people. It can mean a group containing elements of anything you want to study, such as objects, events, organizations, countries, species, organisms, etc.

Necessity: Sometimes it’s simply not possible to study the whole population due to its size or inaccessibility.
Practicality: It’s easier and more efficient to collect data from a sample.
Cost-effectiveness: There are fewer participant, laboratory, equipment, and researcher costs involved.
Manageability: Storing and running statistical analyses on smaller datasets is easier and reliable.

A parameter is a measure that describes the whole population.
A statistic is a measure that describes the sample.
You can use estimation or hypothesis testing to estimate how likely it is that a sample statistic differs from the population parameter.

A sampling error is the difference between a population parameter and a sample statistic. In your study, the sampling error is the difference between the mean political attitude rating of your sample and the true mean political attitude rating of all undergraduate students in the Netherlands.
Sampling errors happen even when you use a randomly selected sample. This is because random samples are not identical to the population in terms of numerical measures like means and standard deviations.
Because the aim of scientific research is to generalize findings from the sample to the population, you want the sampling error to be low. You can reduce sampling error by increasing the sample size.