Mean, median, and mode are the three measures of central tendencies. Mean is the common measure of centraltendency, most widely used in calculations of averages.

**Measures of Central Tendencies**

Mean, median, and mode are the three measures of central tendencies. Mean is the common measure of centraltendency, most widely used in calculations of averages. It is least affected by sampling fluctuations. The mean of a number of individual values (X) is always nearer the true value of the individual value itself. Mean shows less variation than that of individual values, hence they give confidence in using them. It is calculated by adding up the individual values (Σx) and dividing the sum by number of items (n). Suppose height of 7 children's is 60, 70, 80, 90, 90, 100, and 110 cms. Addition of height of 7 children is 600 cm, so mean (X) = Σx/n = 600/7 = 85.71. Median is an average, which is obtained by getting middle values of a set of data arranged or ordered from lowest to the highest (or vice versa). In this process, 50% of the population has the value smaller than and 50% of samples have the value larger than median. It is used for scores and ranks. Median is a better indicator of central value when one or more of the lowest or the highest observations are wide apart or are not evenly distributed. Median in case of even number of observations is taken arbitrary as an average of two middle values, and in case of odd number, the central value forms the median. In above example, median would be 90. Mode is the most frequent value, or it is the point of maximum concentration. Most fashionable number, which occurred repeatedly, contributes mode in a distribution of quantitative data. In above example, mode is 90. Mode is used when the values are widely varying and is rarely used in medical studies. For skewed distribution or samples where there is wide variation, mode, and median are useful. Even after calculating the mean, it is necessary to have some index of variability among the data. Range or the lowest and the highest values can be given, but this is not very useful if one of these extreme values is far off from the rest. At the same time, it does not tell how the observations are scattered around the mean. Therefore, following indices of variability play a key role in biostatistics.

In addition to the mean, the degree of variability of responses has to be indicated since the same mean may be obtained from different sets of values. Standard deviation (SD) describes the variability of the observation about the mean. Todescribe the scatter of the population, most useful measure of variability is SD. Summary measures of variability of individuals (mean, median, and mode) are further needed to be tested for reliability of statistics based on samples from population variability of individual. To calculate the SD, we need its square called variance.

**Variance is the average square deviation around the mean and is calculated by**

Variance = Σ(x-x-) 2/n OR Σ(x-x-) 2/n-1, now SD = √variance.

SD helps us to predict how far the given value is away from the mean, and therefore, we can predict the coverage of values. SD is more appropriate only if data are normally distributed. If individual observations are clustered around sample mean (M) and are scattered evenly around it, the SD helps to calculate a range that will include a given percentage of observation. For example, if N ≥ 30, the range M ± 2(SD) will include 95% of observation and the range M ±3 (SD) will include 99% of observation. If observations are widely dispersed, central values are less representative of data, hence variance is taken. While reporting mean and SD, better way of representation is ‘mean (SD)’ rather than ‘mean ± SD’ to minimize confusion with confidence interval.

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