In this article you will learn sampling error vs sampling bias with the help of examples.
When conducting statistical research, sampling bias and errors can be seen when estimating the true value of variables. These two are frequently mistaken for one another.
Sampling bias is a result of choosing a sample that is not random or representative of the entire population.
It occurs when a systematic investigation’s data sample does not fairly reflect the data that can be obtained in the research setting. Sampling bias occurs when data is collected in a way that causes some members of the target population to have a lower or greater sampling probability than others.
It is a common error because it frequently occurs unintentionally, that is, without the researcher’s knowledge. Many times, your research methodology and research design might introduce sample bias into your data collection process, changing the results of your research.
Examples of Sampling Bias
One crew stated they did not enjoy walking, so they abandoned their list of randomly chosen clusters and decided to solely weigh and measure the kids who lived in the nearby villages.
The survey crew had to weigh and measure the numerous children in several communities at the insistence of the leaders. These data were incorporated into the survey by the survey teams. The leader’s children are undoubtedly better off than most youngsters. Due to this, the prevalence of malnutrition would likely be understated as a result. The survey team neglected to include households that belonged to an ethnic minority, when compiling lists in a number of villages. People in this minority group frequently face discrimination and are generally prohibited from using the village borehole. Instead they have to get their water from ponds or streams close by. This would result in an underestimation of the frequency of recent diarrheas. Since people in ethnic minorities, particularly children, are likely more exposed to organisms that cause diarrheas.
Sampling error is the difference between a survey’s result and population value as a result of the sample’s random selection of people or households. It is the difference between the chosen sample’s traits, behaviors, qualities, or figures and the population’s actual characteristics, traits, behaviors, or figures.
In contrast to bias, sampling error can be anticipated, calculated, and taken into consideration.
Measures of Sampling Error
There are various ways to measure sampling error.
- P values
- Standard error
- Coefficient of variance
- Confidence intervals
These measures are used to:
✔ Calculate sample size prior to sampling
✔Determine how sure we are of result after analysis
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Sampling Bias vs Sampling Error
|Sampling Bias||Sampling Error|
|It occurs if you select the wrong sample so that it’s not representative.||It occurs if you have a small sample size resulting in poor precision|
|Can be estimated precisely after the fact.||Can be controlled by choosing the right sampling technique and sample size.|
|Cannot be measured exactly.||Can be estimated precisely after the fact.|
|Careful attention is necessary for prevention and control.||Unavoidable if less than 100% of the population is sampled.|
|Example: One team misunderstood the instructions and decided to include children up to 10 years old rather than just those between 6 – 9 months old.||Example: Sampling error occurs if the confidence intervals were wide due to small sample size,|
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