Consider a time when you took an exam in school and received your results. Your score meant more when you also knew how everyone else performed in comparison. More than just knowing the average score, maybe you were also able to see how many students scored higher and lower than you.
Measures of central tendency and variability can offer insight into large data sets. They differ, however, in that central tendency can tell you an average or typical data point (e.g., exam score average), whereas variability can tell you even more, such as how far each data point differs from the average or mean as well as how spread out around the mean all of the data points are. How much a score deviates from the mean can help you better understand a group of scores or numbers that you want to summarize or describe. Consider, for example, that one well-paid individual’s salary is $100,000, while the majority of salaries are closer to $50,000. How might that one high score influence each measure of central tendency? Would it cause you to believe one measure of central tendency is a better summary of salary than another? These questions are important ones to consider as both consumers and producers of research. In this Discussion, you evaluate the choices of using different measures of central tendency and variability to summarize data.
Consider the following two scenarios. In scenario one, you are a restaurateur, seeking to open a restaurant. In the other scenario, you are hoping to attract a restaurant to your town. Consider how measures of central tendency and variability can help you communicate information about data and think about why.
Post a response to the following: As the restaurateur, what measures of central tendency and variability would you want to know about before making your decision? Would one measure of central tendency be more important to you than another? Why?
As an individual hoping to attract a restaurant to your town, would your response be the same? Explain why or why not and provide an example.