A brief overview of statistical errors and why they are difficult to notice
Statistical errors are deviations that arise during statistical analysis, resulting in inaccurate results and inferences. These errors may stem from various factors, including measurement inaccuracies or incorrect assumptions about data distributions.
Statistical mistakes are categorised into two types: Type I and Type II. Understanding statistical errors requires distinguishing between Type I and Type II errors.
Type I errors arise when the true null hypothesis is incorrectly rejected (false positive),
Type II errors arise when a false null hypothesis is not rejected (false negative).
Identifying statistical errors can be challenging for several reasons:
- First, statistical analysis usually requires complex calculations and methodologies that are not always transparent. Many statistical tests depend on assumptions about data distribution, sample representativeness, or independence, which may not be valid in real-world scenarios.
- Statistical errors (especially Type I errors) can appear subtly and may be challenging to identify without a thorough grasp of statistical theory. For example, a significant p-value influenced by sample size could cause researchers to accept a null hypothesis and make wrong conclusions mistakenly.
- Statistical interpretation can also be ambiguous and subject to individual interpretation. Hence, human bias could also contribute to overlooking statistical errors and interpreting data in a way that supports a particular hypothesis.
To avoid statistical errors, every researcher needs a solid grasp of statistical concepts, a discerning approach to data analysis, and knowledge of potential causes of error in statistical analysis.
