Therefore, we can say that the mean of our dataset is 2. Our calculations show that when we share the total number of movie-watchings equally with all of the students in the class, each student would have watched the movie 2 times. Now, dividing the total number of times the movie was watched, 52, by the number of students surveyed, will give us the value of the mean: If you count the number of entries we have in our dataset, you’ll find that there are 26. This is exactly the type of problem we are trying to solve when sharing something equally, so division is the correct operation for the job. Remember that when we do a division problem like 12 / 4, we are answering the question “If I split 12 evenly into 4 groups, how much is in each group?” Now that we have the total number of times the movie was watched in the class, we can go on to the next step, which is to divide this total by the number of students surveyed in the class. We can follow a similar two-step process to compute the mean of our dataset.įirst, we want to find out how many times the movie was watched by the class in total. If you wanted to share something equally with your friends, you might first find out how much of that thing you all have in total, and then you could split up that total so that you and your friends all get the same amount. Let’s go into more detail about what that means. We can interpret the mean as the result of “sharing” all of the data equally across the dataset. The first measure of central tendency we’ll discuss is the mean, which is also known as the average. To answer that question, we’ll use measures of central tendency. Something you might want to know is how many movies the “typical” student watched. Looking at this graph, you might notice that most students watched the movie two or fewer times, but there are a few students who watched the movie more than twice. The height of each bar in this graph represents the number of times a student watched the movie. It is hard to extract much information when the data is presented as just a list of numbers, so let’s visualize the data using a bar graph. Let’s imagine you survey everyone in your class and ask each person how many times they’ve watched a certain movie. To get a better understanding of the mean, median, mode, and range, it helps to work with an example data set. While they don’t give us the full picture, measures of central tendency and dispersion are valuable information when doing data analysis. By using measures of central tendency and measures of dispersion, we can get an idea of both the middle of the dataset as well as its extremities. The range is an example of a measure of dispersion. A measure of dispersion is used to approximate how spread out a dataset is (think dispersion → dispersed/spread out). The mean, median, and mode are examples of measures of central tendency. These two categories are measures of central tendency and measures of dispersion.Ī measure of central tendency is used to approximate the center of a dataset (think central → center). In this blog, we’ll cover the mean, median, mode, and range, which are four different numbers that you can use to help you understand the datasets you encounter in school and in your own life.īefore we talk about these four specific statistics, let’s talk about two important categories of statistics that can help us understand different aspects of datasets. Thankfully, mathematics gives us tools to help us summarize large sets of data with just a few numbers. It’s easy to be overwhelmed by the sheer quantity of data, especially with modern technology making more and more data available to us every day. Weather reports, sports statistics, and your grades in school: these are just a few examples of data that you might encounter in your day-to-day life. If you examine the world around you, you’ll find that data is everywhere.
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