Final answer:
Ignoring outliers can result in inaccurate statistical analysis as they may significantly influence the results, such as affecting the slope of the regression line and correlation coefficient.
Step-by-step explanation:
It is important not to just ignore outliers as they can have a significant impact on statistical analyses. An outlier is a data point that stands out from the rest of the dataset and can be an indication of measurement error or a signal of something new or different about the population being studied. Identifying whether an outlier is due to an error or holds important information requires careful examination.
Outliers can also influence the results of regression analysis, including the slope of the best-fit line and the correlation coefficient, r. Outliers may flatten the slope of the line of best fit, making it less accurate as a predictor. A potential outlier that lies a vertical distance of 2s (where s is the standard deviation of the residuals) from the line of best fit could be considered too far from the model to be representative of the data set. Therefore, when analyzing data, it is crucial to scrutinize outliers closely to decide whether they should be included in the analysis.