What is the primary risk associated with bias in sampling, and how can it be minimized?

Bias in sampling occurs when certain members of the population are either overrepresented or underrepresented, leading to results that do not accurately reflect the true population characteristics. This can cause false conclusions that do not generalize to the larger population. One of the most effective ways to minimize bias is to use random sampling, where every member of the population has an equal chance of being selected. Random sampling helps to avoid the selective inclusion of certain types of individuals, thereby ensuring that the sample is representative and reducing the potential for systematic errors in the analysis. The other options are incorrect because: • Option 2 (Data redundancy) is not a direct consequence of bias in sampling. Bias impacts representation, not redundancy. • Option 3 (Accuracy) is incorrect; bias typically reduces the accuracy of results, not increases it. • Option 4 (Overfitting) and Option 5 (Underfitting) refer to issues in model development, not directly to sampling bias.