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4.9.1 Sampling Error vs Non sampling errors

Introduction

  • Objective: Understanding the distinction between sampling and non-sampling errors in statistical inference.

Sampling Errors

  • Definition: Variations between the sample and the population that arise due to the random nature of sample selection.
  • Nature: These errors are expected and quantifiable. They decrease as the sample size increases, due to the law of large numbers.

Non-sampling Errors

  • Types:
  • Coverage Error: Occurs when some groups in the population are excluded from the sample, leading to a sample that is not representative of the entire population.
  • Non-response Error: Arises when certain segments of the population do not respond to surveys, causing an underrepresentation or overrepresentation of these groups.
  • Measurement Error: Results from data collection issues, such as ambiguous survey questions or respondents not being truthful or misunderstanding questions.

Managing Errors

  • Strategies to Minimize Errors:
  • Define the target population clearly and ensure the sample represents this population.
  • Design the data collection process carefully and train collectors to avoid biases.
  • Conduct pilot tests to identify and mitigate potential sources of error before full-scale data collection.
  • Use appropriate sampling methods to ensure all relevant segments of the population are included.

Conclusion

  • Summary: This module has explored the connection between sample data and probability distributions, introducing sampling distributions as the foundation for statistical inference. We've covered various sampling methods, the properties of point estimators, and how these contribute to understanding population parameters.
  • Looking Ahead: Subsequent modules will continue to build on these concepts, focusing on deriving confidence intervals and testing hypotheses to make informed decisions based on sample data.
  • Closing Remarks: Thank you for your attention to this crucial aspect of statistical analysis. By understanding and minimizing both sampling and non-sampling errors, we can make reliable inferences that accurately reflect the larger population.
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