# Sampling Errors in Research

Error is defined as , “an act, assertion, or belief that unintentionally deviates from what is correct, right, or true”. In a business research process, there is sure to be some error in the results because there is the involvement of human intelligence and the use of sampling methods that may not be always accurate. The absolute value of the difference between an unbiased point estimate and the corresponding population parameter is known as a sampling error. It arises because the data is collected from a part, rather than the whole of the population. The sampling error can be more reliable by increasing the sample size. Total survey errors are of two types: Random sampling error & non-sampling error.

1. Random Sampling Error: Random sampling error or sampling error is the difference between the sample results and the results of a census conducted by identical procedures. Although a representative sample is taken, there is always a slight deviation between the true population value and the sample value. This is because the sample selected is not perfectly representative of the test population. Therefore, a small random sampling error is evident. As the sampling error is the outcome of chance, the laws of probabilities are applicable to it. The sampling error is inversely proportional to the sample size. As the sample size increases, the sampling error decreases. Although sampling errors cannot be avoided altogether, they can be controlled through careful sample designs, large samples, and multiple contacts to assure representative response. Random sampling error represents how accurately the sample’s true mean value(x sample), is representative of the population’s true mean value(X population).
2. Non-Sampling error: Non- sampling errors also known as systematic errors occur due to the nature of the study’s design and the correctness of execution. Non-sampling error includes non-observation errors and measurement errors. Non- observational errors occur when data cannot be collected from the sampling unit or variable. Measurement errors arise from various sources like respondents, interviewers, supervisors, and even data processing systems. Non-observation error is further divided into non-coverage and non-response error. In probability sampling, each element of the population has a non-zero chance of selection into the sample. Non-coverage error occurs when an element in the target population has no chance of being selected into the sample. Non-response error occurs when data cannot be collected from the element actually selected into the sample. This may be due to the refusal of the element to cooperate because of language barrier, health limitation, or non availability of the element during the survey period. Selection of faulty sampling frame may also result in a non-sampling error. Sampling frame error is said to occur when certain non potential respondents are included in the sampling frame and certain deserving respondents are rejected.