Explain selection bias (with regard to a dataset, not variable selection). Why is it significant? How can data management procedures such as missing data handling make it worse?

Data Science, AI and ML
#1

Selection bias occurs when people, groups, or data are chosen for study in such a way that appropriate randomization is not achieved, resulting in a sample that is not representative of the population.

Selection bias is essential to recognize and understand because it can distort results and offer misleading information about a demographic group.
The following are examples of selection bias:
Sampling bias: a skewed sample resulting from non-random sampling

Time interval: picking a time period that is conducive to the intended outcome Conducting a sales study near Christmas, for example.

Exposure: Clinical susceptibility bias, protopathic prejudice, and indication bias are all examples of bias.

Data: involves cherry-picking, evidence suppression, and the incomplete evidence fallacy.

Attrition: Attrition prejudice is comparable to survivorship bias, in which only those who ‘survived’ a lengthy process are considered, or failure bias, in which only those who ‘failed’ are considered.

Observer selection: linked to the Anthropic principle, which states that whatever evidence we gather about the cosmos is filtered by the fact that it must be compatible with the conscious and sapient life that sees it in order to be observable.

Because various approaches influence the data in different ways, handling missing data might exacerbate selection bias. If you substitute null values with the data mean, for example, you’re creating bias since you’re thinking the data isn’t as spread out as it actually is.