Central tendency is a descriptive summary of a dataset through a single value that reflects the center of the data distribution. Along with the variability (dispersion) of a dataset, central tendency is a branch of descriptive statistics.

The central tendency is one of the most quintessential concepts in statistics. Although it does not provide information regarding the individual values in the dataset, it delivers a comprehensive summary of the whole dataset.

Measures of Central Tendency

Generally, the central tendency of a dataset can be described using the following measures:

Mean (Average): Represents the sum of all values in a dataset divided by the total number of the values.

Median: The middle value in a dataset that is arranged in ascending order (from the smallest value to the largest value). If a dataset contains an even number of values, the median of the dataset is the mean of the two middle values.

Mode: Defines the most frequently occurring value in a dataset. In some cases, a dataset may contain multiple modes while some datasets may not have any mode at all.

Even though the measures above are the most commonly used to define central tendency, there are some other central tendency measures, including, but not limited to, geometric mean, harmonic mean, midrange, and geometric median.

The selection of central tendency as a measure depends on the properties of a dataset. For instance, mode is the only central tendency measure of categorical data while a median works best with ordinal data.

Although mean is regarded as the best measure of central tendency for quantitative data, it is not always the case. For example, mean may not work well with quantitative datasets that contain extremely large or extremely small values. The extreme values may distort the mean. Thus, you may consider other options of central tendency.

The measures of central tendency can be found using a formula or definition. Also, they can be identified using a frequency distribution graph. Note that for the datasets that follow a normal distribution, the mean, median, and mode are located on the same spot on the graph.

CFI is the official provider of the global Financial Modeling & Valuation Analyst (FMVA)™ certification program, designed to help anyone become a world-class financial analyst. To keep advancing your career, the additional resources below will be useful:

Comprehensive List of Excel Functions

Dynamic Dates, Sum, Average and Scenarios

Quantitative Analysis

Standard Deviation

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ResultView Original Central tendency may be a descriptive summary of a dataset through one value that reflects the middle of the info distribution. Along side the variability (dispersion) of a dataset, central tendency may be a branch of descriptive statistics.

The central tendency is one among the foremost quintessential concepts in statistics. Although it doesn’t provide information regarding the individual values within the dataset, it delivers a comprehensive summary of the entire dataset.

Measures of Central Tendency

Generally, the central tendency of a dataset is often described using the subsequent measures:

Mean (Average): Represents the sum of all values during a dataset divided by the entire number of the values.

Median: the center value during a dataset that’s arranged in ascending order (from the littlest value to the most important value). If a dataset contains a good number of values, the median of the dataset is that the mean of the 2 middle values.

Mode: Defines the foremost frequently occurring value during a dataset. In some cases, a dataset may contain multiple modes while some datasets might not have any mode in the least .

Even though the measures above are the foremost commonly wont to define central tendency, there are another central tendency measures, including, but not limited to, mean , mean , midrange, and geometric median.

The selection of central tendency as a measure depends on the properties of a dataset. As an example , mode is that the only central tendency measure of categorical data while a median works best with ordinal data.

Although mean is considered the simplest measure of central tendency for quantitative data, it’s not always the case. For instance , mean might not work well with quantitative datasets that contain extremely large or extremely small values. The acute values may distort the mean. Thus, you’ll consider other options of central tendency.

The measures of central tendency are often found employing a formula or definition. Also, they will be identified employing a distribution graph. Note that for the datasets that follow a traditional distribution, the mean, median, and mode are located on an equivalent spot on the graph.

CFI is that the official provider of the worldwide Financial Modeling & Valuation Analyst (FMVA)™ certification program, designed to assist anyone become a world-class securities analyst . To stay advancing your career, the extra resources below are going to be useful:

Comprehensive List of Excel Functions

Dynamic Dates, Sum, Average and Scenarios

Quantitative Analysis

Standard Deviation