Sampling and Sources of Bias

A census is when we sample the entire population.

It is difficult to take a census.

Sampling Bias

There are multiple reasons for sampling bias:

Non-response can occur if only a small fraction of the sample respond to a survey, the sample may no longer be representative of the population.

Voluntary response occurs when the sample consists of people who volunteer to respond becuase they have strong opinions, causing the responses to not be representative of the population.

Convenience samples are samples with a higher proportion of people who are more easily accessible than the complete population.

It is possible to have a large sample, but for that sample to have a bias that leads to significant issues with the conclusions we can draw from the sample.

Almost all statistical methods are based on the notion of implied randomess.

Common Sampling Techniques

Simple Random Samples randomly select cases from the entire population, where there is no implied connection between the points selected.

Stratified Samples are samples made up of random samples from non-overlapping subgroups. Each subgroup is called a stratum (plural, strata).

Cluster Samples are samples where the researcher divides the population into groups called clusters. Subgroups are created such that each group should have a similar population. When the clusers are created, we sample a simple random sample from within each cluster.

Random Assignment vs Random Sampling

Random Assignment No Random Assignment
Random Sampling Causal and Generalizable (Ideal Experiment) Not Causal but Generalizable (Most Observational Studies)
No Random Sampling Causal but not Generalizable (Most Experiments) Neither Causal nor Generalizable (Bad Observational Studies)