For most of the research studies, data collection is done using a random sample and not on the entire population. However, a selected sample may be biased at times. This biasness and other causes may lead to sampling errors.
This type of an error is the sample’s deviation from the true qualities, characteristics and behaviours of the whole population. A major cause leading to a sampling error is individual differences. While the population is same, specially chosen subjects may have individual differences in their traits and behaviours. The sample is actually a subset of your population and may not match it every time.
Additionally, researchers are sometimes partial in choosing their samples. This may also lead to a sampling error that can be corrected in case a researcher takes care while choosing his/her sample. It is under your control not to be biased while selecting your own study sample. On the other hand, a sampling error may also occur due to chance. While randomly choosing a sample reduces the chances of such an error, it still has some probability to occur. While any of these factors may cause a sampling error, it will ultimately lead to a systematic error.
This means that the results obtained from an erroneous sample would significantly differ from those obtained by studying the whole population. The smaller the sample, the higher is the chance of an error. While it is not possible to study an entire population, it is best to take up a sample as large as possible. This is the only way to reduce a sampling error. Apart from this, you should always perform unbiased sampling in a random manner. This will also raise the probability of getting a better representative sample for your research study.
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