Final answer:
In Data 1, the value of 7 is potentially an outlier if the majority of values are between 2 and 4. Data 2's skewness might accommodate higher values, making a 7 less likely an outlier. Comparing range, IQR, and standard deviation in both sets informs outlier likelihood, with box plots and standard deviation being useful tools for outlier identification.
Step-by-step explanation:
An outlier is a value in a data set that is significantly distant from the other observations. It may result from variability in the measurement or it could indicate experimental error; the latter sometimes provides a reason to exclude the outlier from the data set.
For Data 1, without specific values provided, we can infer potential outliers by analyzing the distribution. Given the information that there are more data values below four than above four, and more values above two, if the distribution of Data 1 mostly centers around these numbers, then a value of 7 could be more on the extreme end, potentially making it an outlier.
Regarding Data 2, if there is skewness in the data, this could affect the likelihood of outliers. A skewed distribution has a longer tail on one side. If Data 2 is positively skewed (tail to the right), the value of 7 might not be an outlier as it may fit within the longer tail of the distribution.
Comparing the variability of both data sets, we would consider the range, interquartile range (IQR), and standard deviation. If Data 1 has a smaller range or IQR compared to Data 2, the value of 7 is more likely to be an outlier in Data 1 since it would be farther away from the central values of the set.