Which aspect does not contribute to sampling error?

Sampling error refers to the difference between a sample statistic and the actual population parameter due to the fact that the sample only represents a portion of the population. Each of the other aspects mentioned can directly influence the level of sampling error in various situations.

Natural variability within samples indicates that even with a well-designed sampling process, different samples drawn from the same population can lead to different results simply due to chance differences in the samples themselves. This inherent variability is a fundamental aspect of sampling error.

Using a biased sampling method contributes to sampling error because it can systematically favor certain outcomes over others, leading to results that do not accurately reflect the population. Bias in the sampling process skews the representation and thus increases error.

The size of the sample also plays a vital role in sampling error. Larger samples tend to yield statistics that are closer to the true population parameters, as they reduce variability and provide a better approximation of the population.

Statistical significance of results, however, does not affect sampling error in the same way. Statistical significance relates to the strength of the evidence against a null hypothesis based on sample data, but it does not inherently change the underlying sampling error itself. It is concerned more with interpreting the results rather than with how those results were generated in terms of sampling.