Collecting samples from the same location in each container, rack, or bin creates sample bias because it systematically favors one portion of the population and may fail to represent the whole. In the Measure Phase, valid data collection depends on obtaining samples that reflect the actual variation present in the population. If the same location is used repeatedly, hidden patterns such as settling, temperature gradients, stacking effects, handling differences, or location-specific defects may distort the sample. Stratified sampling across machines, shifts, or operators is usually done specifically to improve representativeness when meaningful subgroups exist. Collecting from various locations in a homogeneous population is also a sound approach because it reduces location bias. Acceptance sampling in continuous flow may be appropriate depending on the plan design and context, but the clearly biased method among the listed choices is repeatedly sampling from the same location. In Six Sigma, sampling bias is dangerous because it produces misleading conclusions and weakens all downstream analysis. Therefore, the correct answer is C, because fixed-location sampling can systematically misrepresent the true population.

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