The median is another measure of central tendency representing the “middle score” in a set. It’s the point in a score distribution in which 50% of the scores fall below it and 50% fall above it. The median is usually represented by Md. but can sometimes be represented by M. Computing the median is a two-step process: List all the scores in order, starting with the highest value and progressing to the lowest. For example: 100, 88, 76, 65, 40, 25,…
The mean is the most commonly used measure of central tendency and is considered a descriptive statistic. In practical terms, it is the sum of all the values in a data set divided by the number of values. It is a single value that can summarize the value of many of the scores in the data set. It represents the middle of the score distribution, hence the term “measure of central tendency.” A simple version of the formula would be:…
Inferential statistics allow us to make decisions about a large group (usually referred to as a population) based upon observations made within a small group selected from that population (usually referred to as a sample. This ability to infer something about a population based upon a sample allows a deeper understanding of data beyond what descriptive statistics alone can offer. Sometimes knowing the mean of a group of scores is enough information. Still, inferential statistics allows us to compare the…
Descriptive statistics are used to organize, summarize and describe the attributes of a set of data. A set of data, especially numeric data, are often referred to as a distribution. A category of descriptive statistics is those measures that describe a distribution using one measure and can be thought of as describing “middle’ or typical score within a distribution. These measures of central tendency, such as the mean, median, and mode, are typically used as the best summary of a…
Why study statistics, and how can I use it in my career or everyday life? We are surrounded by data and inundated with it every waking hour, and while we are asleep, mountains of data accumulate, ready to grab our attention. What do governments, industries of all kinds, schools and universities, and even sports teams have in common? They collect and utilize massive amounts of data. How do these groups make sense of all this information and find patterns and…