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The arithmetic mean is calculated by adding all values in the data set and dividing by the total number of values, n. For example, to find the mean daily screen time of students, sum all individual screen times and divide by the number of students. This formula ensures every data point contributes equally to the final average.

The sample mean, denoted by x bar, is calculated from a subset of data selected at random, while the population mean, denoted by mu, is calculated from all data in the entire population. For instance, measuring screen time from a few students yields a sample mean, whereas measuring all students in a school yields the population mean.

The arithmetic mean is sensitive because it considers every data value in its calculation. A single extreme value, or outlier, can significantly change the mean and make it unrepresentative of the data. For example, if nine students are aged 20-21 but one is ten years old, the mean becomes artificially low and misrepresents the central tendency.

No, the arithmetic mean cannot be calculated for qualitative data. Qualitative data consists of non-numerical categories or attributes. For example, in a class of students with different nationalities, there is no meaningful arithmetic mean for nationality because these are categorical values, not numerical ones.

When data values are not unique, the sum can be calculated by multiplying each distinct value by its frequency, then dividing by the total number of observations. This approach simplifies calculations when multiple data points have the same value. The mean from frequency distribution accounts for how often each value appears in the data set.

The arithmetic mean is representative because it incorporates every data value in its calculation, providing a comprehensive summary of the data set. This inclusion of all values makes it a reliable measure of central tendency for most data sets, though outliers can compromise its representativeness in certain situations.

Avoid using the arithmetic mean when the data contains significant outliers that distort the average, or when analyzing qualitative data that cannot be numerically averaged. Careful consideration is required before applying the arithmetic mean, as extreme values or non-numerical categories can make it an inaccurate representation of the data's true center.