Sampling Error:
Sampling error is the difference between the characteristics of a sample and the characteristics of the population from which the sample was drawn.

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It occurs because a sample is only a subset of the entire population, and there is variability in the data. Sampling error is a natural and expected part of the sampling process and can be quantified using statistical measures.

Major Non-Sampling Errors:

Selection Bias:

Description: Selection bias occurs when the process of selecting the sample is not random, leading to an unrepresentative sample that does not accurately reflect the population.
Example: In a telephone survey, if only households with landline phones are included, individuals who only have mobile phones are excluded, leading to a biased sample.

Non-Response Bias:

Description: Non-response bias occurs when the individuals who choose not to participate in a survey or study have different characteristics from those who do participate, leading to an unrepresentative sample.
Example: In an online survey about consumer preferences, if younger individuals are more likely to respond than older individuals, the sample may not accurately represent the entire population’s preferences.

Non-sampling errors can significantly impact the validity and reliability of study results. Addressing these errors involves careful consideration of the sampling design, survey administration methods, and strategies to minimize biases in data collection.