Six Sigma Green Belt Certification Practice Exam

The difference between the result obtained from a sample and the actual population value is known as bias. Bias occurs when there is a systematic error in the sampling method that leads to the sample not being representative of the overall population. This can happen due to various reasons, such as selecting a non-random sample or having measurement errors that consistently skew the results in a particular direction.

Understanding bias is crucial in statistical analysis because it impacts the validity of the conclusions drawn from the sample data. If bias is present, the findings may not accurately reflect the population, leading to incorrect decisions based on flawed data.

In contrast, concepts like correlation, precision, and accuracy relate to different aspects of data analysis. Correlation measures the relationship between two variables, precision refers to the consistency or repeatability of measurements, and accuracy indicates how close a measured value is to the true value. While these terms are important in statistical analysis, they do not specifically describe the difference between a sample result and the actual population value.