Which factor can significantly skew statistical results in data analysis?

Outliers within a data set can significantly skew statistical results because they are extreme values that do not conform to the overall pattern of the data. When conducting data analysis, these outliers can disproportionately influence measures of central tendency, such as the mean, as well as measures of variability. This impact can lead to misleading interpretations if not addressed appropriately.

For example, if you're calculating the average income of a group and a few individuals have extremely high incomes, the mean will be skewed upward, making it appear that the average income is higher than it truly is for the majority of the group. This can lead to flawed decisions based on inaccurate data representation.

In contrast, normal data distributions, consistent data trends, and sample sizes that are too large typically provide stable and reliable results. Normal distributions assume a bell-shaped curve, which allows for standard statistical methods to be applied. Consistent trends facilitate predictions and understandings of data behavior over time, while large sample sizes enhance the reliability of results by reducing variability and the margin of error. Thus, outliers uniquely possess the potential to distort the outcome of data analysis, making them a critical factor to consider.