In statistical analysis selection bias means that the sample you have chosen is not representative of the population you want to look at.
Let me put this way for you my dear followers selection bias is making general assumptions that are not true for the general population because the people you selected to look at were special. For example, you could say that going to the hospital is extremely dangerous because most people die at hospitals. This assumption is of course false. People die at hospitals because sick and injured people go to the hospital. For your observation People die at the hospital you only selected a small special group, namely people who were already very sick or injured that they went to the hospital. This is selection bias.
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Selection of bias types
There is a long list of statistical bias types. This section covers those that can most affect the job of a data scientist. These are:
- Selection bias.
- Self-selection bias.
- Recall bias.
- Observer bias.
- Survivorship bias.
- Omitted variable bias.
- Cause-effect bias.
- Funding bias.
- Cognitive bias(e.g. Confirmation bias).
How to avoid selection biases?
- Using random criteria when selecting samples from populations.
- Ensuring that the subgroups selected are equivalent to the population at large in terms of their key characteristics.
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