1. Bias: Non-response can introduce bias into the sample, as the characteristics of non-respondents may be different from those who do respond. This can affect the representativeness of the sample.
2. Decreased precision: Non-response can lead to decreased precision in the estimates, as the sample size becomes smaller and potentially less representative of the population.
3. Missing data: Non-response leads to missing data, which can complicate the analysis and interpretation of the results.
4. Reduced generalizability: Non-response can reduce the generalizability of the findings, as the sample may not accurately represent the population of interest.
5. Measurement errors: Non-response can introduce measurement errors, as non-respondents may have different response tendencies than respondents. This can affect the accuracy and reliability of the data.
6. Inefficient use of resources: Non-response can cause a waste of resources, as efforts to collect data from non-respondents are often unsuccessful and time-consuming.
7. Non-response bias: Non-response can introduce non-response bias, where the responses of those who do not participate are systematically different from those who do. This can lead to incorrect inferences and conclusions.
8. Difficulty in analysis: Non-response can make the analysis more challenging, as it requires the use of complex statistical methods to deal with missing data and non-response biases.
9. Increased non-sampling errors: Non-response increases the likelihood of non-sampling errors, such as non-coverage and non-response errors, which can further impact the accuracy of the estimates.
10. Limited ability to address non-response: In some cases, it may be difficult or impossible to address non-response adequately, particularly if the non-respondents are a large proportion of the sample or the reasons for non-response are unknown. This can limit the ability to draw meaningful conclusions from the data.
10 problems of non response in statistics
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