What are the measures of central tendency
What are the measures of central tendency

This article "What are the measures of central tendency?" will help you to understand about what are the five measure of central tendency?

What are the measures of central tendency?


A measure of central tendency is derived by taking a sample of items as provided in an individual’s set of measuring instruments and plotting each item’s mean on a sheet of paper. It can be calculated using these simple formula, which I’ll explain below:

What are the measures of central tendency
What are the measures of central tendency

Measures of central tendency are used to summarize or characterize the items of a scale or survey that you have collected and that includes all scores in a larger group of respondents. Most commonly, this analysis might be conducted on large studies such as surveys or polls. They may also be useful for smaller samples that are smaller. (For example, a small study might have hundreds of respondents who were asked several items on a test.) These measures can be used to summarize large groups of individuals, such as college students. For instance, the mean score on one measure of my favorite professor would indicate that the average student there really liked him, even though he wasn’t exactly telling me the truth. (A much more complicated example might show that the average American prefers not to see many public places because it makes them nervous.) The most common measure of central tendency is the mean, but there is another popular formula to calculate its quotient or weighted mean that takes into account the order and frequency of items, like the following formula: Average = sum of all measures, where “Average = sum(m)” means sum of all items and “m” means measure. Another common form is to calculate the standard deviation, or the deviation between the measure’s value and its mean. This formula is as follows: Std. Deviation = SD/SDm = 1-((mean- std. or stg.), to add the square root of the squared deviation: Standard deviation = sqrt[(m- std.): std.m]


The use of standardized measures allows for comparisons over a broad range of scores by adding up all values of the same item, if applicable, without any effort to select individual scores. Some researchers recommend comparing the effect of treatments on scores across multiple measurements, so the standard deviation and mean are often helpful for assessing whether an intervention may have contributed significantly to change in a given condition. However, it’s important to note that the standard deviation is often misleading. If your scales showed only modest differences in mean scores, the standard deviation could be large, and this could produce a large difference in scores, even though the true difference between scores is small because the treatment was actually ineffective. To avoid this error, you must look at the correlations across different measures.


Another possible error is the presence of outliers. Sometimes, people report scores above or below their actual measurement, which can distort how much each score is worth. An outlier is a respondent whose results differ substantially from those that follow your normal distribution. Outliers are common in surveys, including in the data for a particular test. People tend to underestimate or overestimate data due to factors other than their own data errors. One way to get rid of outliers is to take the median of any of your measures, or simply take the first or last one below it. A higher-than-median score should provide little cause for concern, while a lower-than-median score indicates strong evidence for a problem. In practice, you typically want outliers to stay out of your analysis even when doing a test, so you can’t just ignore them. You can always replace outliers with non-significant ones after you’re finished running the analysis. The best solution is to run experiments within your limits and then determine what the result is, and make decisions about your design.