Mean, Median & Mode: Measures of Central Tendency

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  • 0:07 Central Tendency
  • 1:50 Mean
  • 3:12 Median
  • 4:20 Mode
  • 5:24 Lesson Summary
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Lesson Transcript
Instructor: Devin Kowalczyk

Devin has taught psychology and has a master's degree in clinical forensic psychology. He is working on his PhD.

By describing the data using central tendency, a researcher and reader can understand what the typical score looks like. In this lesson, we will explore in more detail these measures of central tendency and how they relate to samples and populations.

Central Tendency

I'm staring at my cat in the window right now and wondering how many cats on average do people own. What is the national average for cats owned by people?

Measures of central tendency is a set of descriptive measures that indicate the typical score. When conducting research, we need or want to know what is most likely going to happen. To say it differently, we sometimes want to know what has the highest probability of occurring. A researcher seeking funding may want to know about a psychological service center's most common diagnosis and what are the problems most likely faced by the clients. If the researcher knows the center deals mostly with smoking and drug addiction, then they most likely would apply for money to help with those services.

The three main types of measures of central tendency are:

  • Mean: defined as the data's average score of the sample
  • Median: defined as the middle score after the scores have been arranged in numerical order
  • Mode: defined as the most often occurring value

When trying to understand how typical or normal data is, we, as researchers, need to differentiate and know the difference between a population and a sample. A population is defined as the complete collection to be studied. So, if you're interested in mental health of your city, a population data set would be all of the people in your city. A sample is defined as a section or part of the population and would be anywhere from 1% to 99% of the city's population.


Mean, or average, is simply all of the scores added up and then divided by the number of scores. This will give you a score that, if you were to graph it, would be somewhere in the middle of all of them. Mean is useful for understanding what score is most likely to happen. When other statistical tests fail and you can't use them, you rely on the mean.

Means in relation to samples and population have to do with accuracy as well as relation. Accuracy has to deal with the sample being representative of the population. Because most researchers work with samples, there is an attempt to have a mean match up closely with the population that you're interested in. To use my cat example: If the national average of cats per household is one and we took a sample of all of my friends and found that the average number of cats between us was three, that means I have a lot of cat friends.

Averages and individuals also have a relationship. If the national average is one, and my friends have an average of three, and I have four cats, that tells us that I have more cats than both the national average and the average of my friends.


Sometimes when collecting information, a researcher will have a group of data that is way off to the side. This is known as an outlier, or a point of data that is distant from the others, either extremely high or extremely low. When you plug it into the mean, it will skew your results. Like if you had someone in your study with 86 cats, they're going to really throw off your results. Because this one person can throw off your results, the next best way to describe your data is median.

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