Q1, Q2, and Q3 are represent the first, second, and third quartile's value.įor ith Quartile = th observation, where i = 1, 2, 3.įor example, in the above, first quartile (Q1) = (n + 1)/4= (15 + 1)/4 = 4 th observation from initial = 88 mmHg (i.e., first 25% number of observations of the data are either ≤88 and rest 75% observations are either ≥88), Q2 (also called median) = 8 th observation from initial = 95 mmHg, that is, first 50% number of observations of the data are either less or equal to the 95 and rest 50% observations are either ≥95, and similarly Q3 = 12 th observation from initial = 107 mmHg, i.e., indicated that first 75% number of observations of the data are either ≤107 and rest 25% observations are either ≥107.
The quartiles are the three points that divide the data set into four equal groups, each group comprising a quarter of the data, for a set of data values which are arranged in either ascending or descending order. A representative value (measures of central tendency) is considered good when it was calculated using all observations and not affected by extreme values because these values are used to calculate for further measures. That is why measures of central tendency are also called as measures of the first order. It helps in further statistical analysis because many techniques of statistical analysis such as measures of dispersion, skewness, correlation, t-test, and ANOVA test are calculated using value of measures of central tendency. To make comparisons between two or more groups, representative values of these distributions are compared. Measures of central tendency give us one value (mean or median) for the distribution and this value represents the entire distribution. The mean, median, and mode are three types of measures of central tendency. Further examples related to the measures of central tendency, dispersion, and tests of normality are discussed based on the above data.ĭata are commonly describe the observations in a measure of central tendency, which is also called measures of central location, is used to find out the representative value of a data set. To understand the descriptive statistics and test of the normality of the data, an example with a data set of 15 patients whose mean arterial pressure (MAP) was measured are given below. In the present study, we have discussed the summary measures to describe the data and methods used to test the normality of the data. Descriptive statistics and inferential statistics both are employed in scientific analysis of data and are equally important in the statistics. There are different methods used to test the normality of data, including numerical and visual methods, and each method has its own advantages and disadvantages. These statistical methods have some assumptions including normality of the continuous data. To draw the inference from the study participants in terms of different groups, etc., statistical methods are used. In inferential statistics, most predictions are for the future and generalizations about a population by studying a smaller sample. The another is inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors and sampling variation). Descriptive statistics are the kind of information presented in just a few words to describe the basic features of the data in a study such as the mean and standard deviation (SD). Summary measures or summary statistics or descriptive statistics are used to summarize a set of observations, in order to communicate the largest amount of information as simply as possible. Statistics which is a science of collection, analysis, presentation, and interpretation of the data, have two main branches, are descriptive statistics and inferential statistics. In place of individual case presentation, we present summary statistics of our data set with or without analytical form which can be easily absorbable for the audience. Usually, it is meaningless to present such data individually because that will not produce any important conclusions. A data set is a collection of the data of individual cases or subjects.