selection bias

USAGE SUMMARY

"selection bias" is a correct and usable term in written English.
It can be used when referring to situations where the results of a process or study are skewed due to the selective choice of data or sample population. For example, "Due to selection bias, the results of the survey were not representative of the population as a whole."

✓ Grammatically correct

Science

News & Media

Academia

Human-verified examples from authoritative sources

Exact Expressions

58 human-written examples

There is also a strong selection bias.

The Economist

Statisticians call this problem selection bias.

The New York Times - Sports

This problem is known as selection bias.

The New York Times

Selection bias: could recruitment be next?

The Guardian

Selection bias seemed to be unlikely.

Nature

An analogous thing is selection bias.

Yale University

Selection bias isn't just an issue for individual companies.

Harvard Business Review

An element of selection bias may be at work.

The Guardian

The selection bias in art occurs for several reasons.

Stanford University

Human-verified similar examples from authoritative sources

Similar Expressions

2 human-written examples

(This might have been attributable to self-selection bias).

The New Yorker

We assessed self-selection bias and confounding.

Preventive Medicine Reports

Expert writing Tips

Best practice

When discussing research, clearly identify potential sources of "selection bias" and explain how they might affect the results. Transparency is key to maintaining credibility.

Common error

Don't overlook the possibility of "selection bias" in your analysis. Even if randomization is used, subtle factors can still lead to skewed results. Always consider and address potential biases in your research design and interpretation.

Linguistic Context

The phrase "selection bias" functions as a noun phrase, typically serving as the subject or object of a sentence. As Ludwig AI confirms, the phrase is grammatically correct and used to identify a distortion in statistical analysis due to non-random selection.

Expression frequency: Very common

Frequent in

Science

65%

News & Media

20%

Academia

15%

Less common in

Formal & Business

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

In summary, "selection bias" is a common and crucial concept in research, statistics, and data analysis. As Ludwig AI confirms, this term is grammatically correct and widely used to describe a situation where a non-random sample skews results. It's most prevalent in scientific and academic contexts, with significant usage in news media as well. To mitigate "selection bias", researchers should prioritize random sampling, acknowledge potential biases, and employ appropriate statistical techniques. Ignoring this bias can lead to inaccurate conclusions and undermine the validity of research findings.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

biased sample

A more general term that refers to a sample that does not accurately represent the population.

sampling error

Focuses on the error introduced during the sampling process rather than the bias itself.

skewed data

Highlights that the data is not evenly distributed or representative.

ascertainment bias

Highlights bias arising from how data is collected or ascertained.

non-response bias

Emphasizes bias resulting from individuals not participating in a study or survey.

preferential sampling

Refers to the practice of favoring certain elements in the sampling process.

self-selection effect

Points to bias caused by individuals choosing to participate based on specific characteristics.

study participation bias

Focuses specifically on bias introduced by who chooses to participate in a study.

cherry-picking data

Highlights the act of selectively choosing data to support a particular viewpoint.

distorted representation

Focuses on the inaccurate portrayal of a population or phenomenon.

FAQs

What is "selection bias" and why is it a problem?

"Selection bias" occurs when the sample used in a study is not representative of the population, leading to skewed results and inaccurate conclusions. It's problematic because it can invalidate research findings.

How can I identify "selection bias" in research?

Look for situations where the method of selecting participants or data could systematically exclude or favor certain groups. Consider factors like self-selection, convenience sampling, or non-random assignment.

What are some strategies to minimize "selection bias"?

Use random sampling techniques whenever possible. Employ stratification to ensure representation across subgroups. Be transparent about the limitations of your sampling method and acknowledge potential biases. Consider using propensity score matching to address selection bias in observational studies.

What's the difference between "selection bias" and "sampling bias"?

The terms are often used interchangeably, but "selection bias" is a broader concept that encompasses any systematic error in how participants or data are selected, whereas "sampling bias" specifically refers to errors arising from the sampling method itself.