sampling bias

USAGE SUMMARY

'sampling bias' is a correct and usable phrase in written English.
It can be used to refer to the use of non-randomized samples that may not accurately represent a population, or may lead to inaccurate or distorted results because of the method of selection. For example, a study of public opinion on a political issue could be biased if the survey only questioned people who identified as a certain political party.

✓ Grammatically correct

Science

News & Media

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

Granted, it's entirely possible that this is because of sampling bias of some variety or another.

TechCrunch

The referendum narrowly passed, demonstrating the importance of sampling bias in accurately predicting election results.

TechCrunch

Nevertheless, this method might introduce sampling bias [117].

Progress in Orthodontics

Big data offers the potential for vanishingly small statistical error but does nothing to eliminate the risk of sampling bias.

TechCrunch

This study was performed at a single tertiary care center, which may cause sampling bias.

Journal of Ophthalmic Inflammation and Infection

Therefore, sampling bias could exist and could potentially influence the results.

EPJ Data Science

Therefore sampling bias might be possible.

Plosone

An amplification and sampling bias may be the cause for the observation.

Plosone

Likelihood-based parameter inference from Markov chain Monte Carlo is prone to sampling bias [26], [29].

Plosone

In order to prevent from sampling bias, only one sequence per individual was kept.

Plosone

Rather, this bias reflects sampling bias at some step of the sequencing reaction.

Plosone

Expert writing Tips

Best practice

When designing a study, clearly define your target population and use a randomized sampling method to minimize the risk of introducing "sampling bias".

Common error

Avoid over-representing easily accessible groups, as this can lead to "sampling bias". Ensure that less accessible populations are also adequately represented in your sample.

Linguistic Context

The phrase "sampling bias" functions as a noun phrase, typically used to identify a specific type of error in data collection and analysis. Ludwig confirms its usage across various contexts, highlighting its role in describing limitations and potential flaws in research methodologies.

Expression frequency: Very common

Frequent in

Science

76%

News & Media

15%

Formal & Business

9%

Less common in

Encyclopedias

0%

Wiki

0%

Reference

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Ludwig's WRAP-UP

In summary, "sampling bias" is a critical concept in research methodology, referring to distortions caused by non-random sample selection. As Ludwig highlights, it's prevalent across scientific and news domains, emphasizing the need for researchers to implement rigorous sampling techniques to ensure representative data. Awareness of potential "sampling bias" is crucial for maintaining the validity and reliability of study results. Employing strategies like randomization, clearly defining target populations, and acknowledging limitations are vital steps in mitigating its effects.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

selection bias

This term is often used interchangeably with "sampling bias" and refers to the distortion of statistical analysis resulting from the method of collecting samples.

ascertainment bias

This type of bias specifically relates to how data is collected, particularly in health-related studies, and how certain data points are favored over others.

non-random sampling

This term describes the situation where not all members of a population have an equal chance of participating in a study, thus leading to biased results.

biased sampling method

Focuses on the methodology that leads to unequal representation in a sample.

unrepresentative sample

Describes a sample that does not accurately reflect the characteristics of the population from which it is drawn.

distorted sample

Indicates that the sample is not a true reflection of the overall population.

flawed sampling

Highlights the presence of errors or weaknesses in the sampling process.

skewed sample

Suggests that certain data points or groups are over-represented in the sample.

selective sampling

Emphasizes the deliberate choice of specific individuals or groups for a study.

biased data collection

Highlights the presence of systematic errors during data collection, leading to a skewed representation of the population.

FAQs

How does "sampling bias" affect research results?

"Sampling bias" can lead to inaccurate conclusions because the sample does not accurately represent the population, skewing the findings. Using a "random sample" can help mitigate this.

What are common causes of "sampling bias"?

Common causes include convenience sampling, where easily accessible individuals are chosen; "self-selection bias", where participants volunteer; and undercoverage, where certain population segments are excluded.

How can I avoid "sampling bias" in my study?

To avoid "sampling bias", use "random sampling methods", clearly define your target population, and strive for a high response rate. Also, be aware of potential sources of bias in your study design.

What's the difference between "sampling bias" and "measurement error"?

"Sampling bias" arises from non-representative samples, while measurement error occurs during data collection due to inaccurate measurements or responses. Both can compromise the validity of research findings.