Becoming a Data Head. Alex J. Gutman

       The mean is the sum of all the numbers you have divided by the count of all the numbers. The effect of this operation is to give you a sense of what each observation in your series contributes to the entire sum if every observation generated the same amount. The mean is also called the average.

       The median is the midpoint of the entire data range if you sorted it in order.

       The mode is the most common number in the dataset.

      Mean, median, and mode are called measures of location or measures of central tendency. Measures of variation—variance, range, and standard deviation—are measures of spread. The location number tells you where on the number line a typical value falls and spread tells you how spread out the other numbers are from that value.

As a trivial example, the numbers 7, 5, 4, 8, 4, 2, 9, 4, and 100 have mean 15.89, median 5, and mode 4. Notice the mean (average), 15.89, is a number that doesn't appear in the data. This happens a lot: the average number of people in a household in the United States in 2018 was 2.63; basketball star LeBron James scores an average of 27.1 points per game.

      It's a common mistake for people to use the average (mean) to represent the midpoint of the data, which is the median. They assume half the numbers must be above average, and half below. This isn't true. In fact, it's common for most of the data to be below (or above) the average. For example, the vast majority of people have greater than the average number of fingers (likely 9.something).

      To avoid confusion and misconceptions, we recommend sticking with mean or average, median, and mode for full transparency. Try not to use words like usual, typical, or normal.

CHAPTER SUMMARY

      In this chapter, we gave you a common language to speak about your data in the workplace. Specifically, we described:

       Data, datasets, and multiple names for the rows and columns of a dataset

       Numerical data (continuous vs. count)

       Categorical data (original vs. nominal)

       Experimental vs. observational data

       Structured vs. unstructured data

       Measures of central tendency

      With the correct terminologies in place, you're ready to start thinking statistically about the data you come across.

NOTES

      1 ¹ There are additional levels of continuous data, called ratio and interval. Feel free to look them up, but we rarely see the terms used in a business setting. And there are situations when the distinction between continuous and count data doesn't really matter. High count numbers, like website visits, are often considered continuous for the purpose of data analysis rather than count. It's when the count data is near zero that the distinction really matters. We'll explore this more in the coming chapters.

      2 ² Here's a quick example of confounding. In a drug trial, if the treatment group consists of only children and no one got sick, you'd be left wondering if their protection from the disease was caused by an effective drug treatment or because children had some inherent protection from the disease. The effect of the drug would be confounded with age. Random assignment between the control and treatment groups prevents this.

      3 ³ “Data Is” vs. “Data Are”: fivethirtyeight.com/features/data-is-vs-data-are