Bias And Its Effect On Sampling Process
What is sample?
A sample is an arbitrary number of people, objects, or units chosen to represent a population based on certain rules or plans, and a population is the sum of the specific characteristics of any particular population.
Why sampling?
When the population is huge or unlimited, it is almost impossible to study all individuals due to lack of time and resources. Therefore, in this case, a part of the population is selected in a way that represents the entire population. The process of selecting such a score is called sampling. Sampling is a necessary condition for any descriptive or exploratory study because it makes the research process economical. In addition, because the amount of operation is small in sampling, field work and data analysis can be quickly and efficiently calculated.
What is Bias?
Bias is a preference or prejudice against a person or a group, especially in an unfair manner. If the sampling method systematically favors certain results, it is called bias. This bias can lead to erroneous results when entering the sampling program, leading to the researchers’ futility.
Mathematically, the bias of on estimator is defined as:
Bias(θ* ) = E( θ*)- θ ,
where θ* is an estimator of θ, an unknown population parameter. If E(θ*)= θ, then the estimator is unbiased. If E( ) ≠ θ then the estimator has either a positive or negative bias. This means, the average estimator generally over estimates (or underestimates) the population parameter.
What leads to a biased sample?
In statistics, sampling bias refers to the bias in which samples are collected in such a way that some members of the target population have a lower sampling probability than others. This results in a biased sample, a non-random sample of the population (or non-human factors) in which all individuals or instances are not selected equally. If this is not taken into account, the results may be incorrectly attributed to the phenomenon being studied rather than to the sampling method.
Or simply put, sampling bias is where errors occur in research and investigations
When a non-linear function of probability is measured from a limited number of experimental samples, even if these samples are really randomly selected from the base population, there is no sampling bias, otherwise bias may occur. This deviation is called “finite sampling deviation”.
Causes of sampling bias.
A common cause of sampling bias is the design of the study or the data collection procedures, which may be beneficial or detrimental to the collection of data from certain categories or individuals or under certain conditions. Whenever researchers use sampling strategies based on judgement or convenience, sampling bias is also particularly prominent. In this strategy, the criteria used to select the sample are related to the target variable to some extent. For example, due to convenience, the researchers who collect data may choose to collect them primarily from those who live nearby, which will bias the sampling further to prevailing opinion.
Example:
If we take a situation in which we wish to study the relationship between the difficulty level of an examination and the academic results from a batch of 1000 students. For this purpose if we take an examination and only 200 students are included in the sample for evaluation then it is crucial that the selection is random. If all the intelligent students or all the dull ones are selected in the sample are taken then the results may be erroneously extreme ones, as in case of intelligent ones the results may be good even in case of a difficult exam while for those dull students the results may be very low even for a simpler one. This leads to false conclusions and unsatisfactory results.
Many a times, subjects are perceived as good or bad by an investigator out of prejudice which may lead to skip some of the subjects out of the research thus deviating the results. For example if we want to study the effect of television on the knowledge base of the villagers in a specific location. And the investigator being prejudiced towards a particular caste or class residing the village and selecting only those favoured by him may give rise to invalid results leading to inaccurate conclusions.
Effects of biased sample on sampling process.
Sampling bias is problematic because the statistics calculated on the sample may be systematically incorrect. Sampling bias can lead to systematic over- or underestimation of corresponding parameters in the population. Since it is practically impossible to ensure complete randomness in sampling, a sampling bias occurs in practice. If the level of misstatement is small, the sample can be considered a reasonable approximation to a random sample. Similarly, if the measured quantities of the samples are not significantly different, the biased samples can still be a reasonable estimate.
