What is meant by "bias" in statistical sampling?

Bias in statistical sampling refers to systematic errors that consistently skew results in a specific direction, leading to outcomes that do not accurately reflect the true characteristics of the population being studied. This can occur due to flaws in the sampling method, such as favoring certain types of respondents or questions that lead participants toward particular answers. When bias is present, the information collected can misrepresent the population as a whole, which is critical to avoid in order to ensure the validity and reliability of statistical conclusions.

This concept is crucial because a well-designed sampling process should aim for randomness and fairness, where every individual in the population has an equal chance of being included. Recognizing and eliminating bias is essential for deriving accurate insights from data and making informed decisions based on that data.