Final answer:
Data that lie beyond the fences are considered outliers. These fences are calculated using the first and third quartiles (Q1 and Q3) and the interquartile range (IQR). Outliers are important to consider in statistical analyses as they can affect measures of central tendency.
Step-by-step explanation:
Data that lie beyond the fences, which are calculated as Q3 + 1.5 × IQR and Q1 - 1.5 × IQR, are considered outliers. The interquartile range (IQR) is the difference between the third and first quartiles, and it measures the spread of the middle 50 percent of the data. Outliers can significantly affect statistical analyses and may need to be examined carefully to determine whether they should be excluded or included in the dataset for further analysis.
To identify outliers in Table 2.86, you would first need to calculate the first quartile (Q1), the third quartile (Q3), and then find the IQR by subtracting Q1 from Q3. The lower fence would be Q1 - 1.5 × IQR and the upper fence would be Q3 + 1.5 × IQR. Any data points outside these fences would be considered outliers. The appropriate measure of center for skewed data is typically the median, as it is less affected by extreme values compared to the mean.