Errors in Sampling - Types, Examples and Concepts
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The document discusses sampling errors and non-sampling errors, defining sampling errors as discrepancies between sample-derived values and actual population values. It explains how these errors arise from inadequate representation of the population and details ways to minimize them, such as increasing sample size and improving sample selection methods. Non-sampling errors are also mentioned, occurring during data collection and impacting the reliability of received information.
Errors in Sampling - Types, Examples and Concepts
- 2. Types of Errors in Sampling Sampling Errors Non-Sampling Errors
- 3. What are Sampling Errors? Sampling errors are statistical errors that arise when a sample do es not represent the whole population. They are the difference between the real values of the population and the values derived by using samples from the population. Sampling errors occur when numerical parameters of an entire po pulation are derived from a sample of the entire population. Since the whole population is not included in the sample, the par ameters derived from the sample differ from those of the actu al population. They may create distortions in the results, leading users to draw i ncorrect conclusions. When analyst do not select samples that represent the entire pop ulation, the sampling errors are significant.
- 4. Sampling Errors It arises due to the fact that only a part of the population has been used to estimate population parameters and to draw inferences about the population. Sampling errors are absent in a Census survey. Sampling errors can be measured for a given sample design and size. The measurement of sampling error is usually called the ‘precision of the sampling plan’. If we increase the sampling size, the precision can be improved, but incre asing the size of the sample has its own limitation, viz., increase in cos t and time. In brief while selecting a sampling procedure, researcher must ensure tha t the procedure causes a relatively small sampling error and helps to c ontrol the systematic bias in a better way
- 5. How to Prevent Sampling Errors • They can be prevented if the analysts select subsets or samples of dat a that can effectively represent the whole population. • Sampling errors are affected by factors such as the size and design of the sample, population variability and sampling fraction. • Increasing the size of samples can eliminate sampling errors. However, to reduce them by half, the sample size needs to be increased. • The population variability causes variations in the estimates derived fro m different samples, leading to larger errors. The effect of population variability can be reduced by increasing the size of the samples so that these can more effectively represent the population.
- 6. Example of Sampling Error • Suppose the producers of Company XYZ want to determine the viewership of a local program that airs twice a week. The produ cers will need to determine the samples that can represent vari ous types of viewers. They may need to consider factors like ag e, level of education, and gender. • For example, people between the age of 14 and 18 will usually have fewer commitments, and most of them can spare time to watch the program twice weekly. On the contrary, people betw een the age of 18 and 35 usually have tighter schedules and wi ll not have time to watch TV. • Hence, it is important to draw a sample proportionately. Other wise, the results will not represent the real population.
- 7. Categories of Sampling Errors • Population Specification Error – Happens when the analysts do not understand who to survey. For example, for a survey of breakfast cereals, the population can be the mother, childr en, or the entire family. • Selection Error – Occurs when the survey participation is self -selected by the respondents implying only those who are int erested respond. Selection error can be reduced by encouragi ng participation. • Sample Frame Error – Occurs when a sample is selected fro m the wrong population data. • Non-Response Error – Occurs when a useful response is not obtained from the surveys. It may happen due to the inability to contact potential respondents or their refusal to respond.
- 8. Non Sampling Error It arises at the collection and preparation of data and thus are present in both the sample survey as well a s the census survey. A non-sampling error is a statistical term that refers to an error that results during data collection, causing the data to differ from the true values. Non sampling error can be reduced by defining the sa mpling units, frame and population correctly and by employing efficient people in the investigations.
- 9. Non Sampling Error • The "errors" result from the mere fact that data in a sample is unlikely to perfectly match data in the univ erse from which the sample is taken. This "error" can be minimized by increasing the sample size. • Non-sampling errors cover all other discrepancies, in cluding those that arise from a poor sampling techni que. • The higher the number of errors, the less reliable th e information. • When non-sampling errors occur, the rate of bias in a study or survey goes up.