Statistical Errors: Meaning, Types & Explanation
✅ Introduction:
Statistical errors are deviations or inaccuracies that occur while collecting, analyzing, interpreting, or presenting data. These errors can lead to misleading conclusions and affect the validity and reliability of the research or study.
Statistical errors are broadly categorized into two main types:
🔹 1. Sampling Errors
🔍 Definition:
Sampling errors occur when a sample does not accurately represent the population. Since a sample is only a subset of the population, some level of error is expected, especially in random sampling.
🧠 Cause:
- Use of a non-representative sample
- Small sample size
- Improper sampling techniques
📌 Example:
If you conduct a survey of 100 students to understand the behavior of 10,000 students, and the 100 students chosen are only from one class, the results may not represent the whole student body.
✅ Can be Reduced By:
- Increasing sample size
- Using better sampling methods (e.g., stratified sampling)
🔹 2. Non-Sampling Errors
🔍 Definition:
Non-sampling errors occur due to mistakes in data collection, recording, processing, or interpretation, regardless of whether a sample or the full population is used.
🧠 Types of Non-Sampling Errors:
a) Measurement Error
- Arises from faulty instruments or inconsistent methods
- Example: A weighing scale not set to zero gives incorrect readings.
b) Response Error
- Occurs when respondents provide incorrect or biased answers
- Example: People may lie about their income in surveys.
c) Non-response Error
- Happens when selected individuals fail to respond
- Example: Sending out 100 surveys and getting only 30 responses.
d) Processing Error
- Caused during data entry, coding, or analysis
- Example: Typing errors or incorrect formula in Excel.
e) Interviewer Bias
- When the interviewer’s behavior or tone influences the respondent’s answer
✅ Can be Reduced By:
- Careful questionnaire design
- Training data collectors
- Pre-testing tools
- Double-checking data entry
🧩 Difference Between Sampling and Non-Sampling Error
| Basis | Sampling Error | Non-Sampling Error |
|---|---|---|
| Occurrence | Due to sample selection | Due to data collection or processing |
| Control | Can be reduced but not eliminated | Can often be avoided with care |
| Source | Random variation | Human errors, system flaws |
| Example | Choosing an unrepresentative sample | Misinterpreting a respondent’s answer |
🎯 Conclusion:
Understanding and minimizing statistical errors is crucial to ensure accuracy and reliability in research and data analysis. While sampling errors are inherent in survey methods, non-sampling errors can be largely controlled with careful planning, training, and validation.
Accurate data leads to sound decisions. Always evaluate potential errors before drawing conclusions from statistics.