TYPES OF STATISTICS

Types of statistics

(i)                 Descriptive Statistics

(ii)              Comparative Statistics

(iii)            Relationship Statistics

(iv)             Inferential Statistics

(v)               Predictive Statistics

 

i) Descriptive Statistics

The status or characteristic of a single group is interpreted through descriptive statistics. The calculate of the average weight or height for students in a class or the mean of each item of physical efficiency tests in a class, is an example of descriptive statistics. Ex. Mean, Standard Deviation

(i)                 Comparative Statistics

The characteristics of two or more groups are compared. For example ‘t’ test, ‘F’ ratio, etc.

(ii)              Relationship Statistics

In relationship statistics, the factors or traits of the same group are correlated.

(iii)            Inferential Statistics

By inferential statistics, the data collected from a sample of subjects are analyzed and the results are used to generalize the characteristic of population from which the sample is drawn.

(iv)             Predictive Statistics

Unknown facts about the individual are predicted from known measurable qualities. The related motor fitness tests can predict the playing ability of a game.

ATTRIBUTES AND VARIABLES

a)      Attributes

An attribute has a non-gradient classification that is no numerical basis of grouping attributes may be in two or more classes. Examples of two class attributes are teachers as men and women. Curriculam as college preparatory and non-college preparatory and pupils as boys and girls illustrations of more than two – class attributed are color of hair or eyes, various nationalities or races and different major fields of study.

b)      Variables

A variables has gradiant-classification that is there is numerical basis of grouping variables are of two types continuous and discontinuous or (discrete). A continuous variable is capable of any degree of subdivision is the ability to measure infinitenal subdivisions. However, in education, the fitness of measurements is usually limited to some convenient number most of the quantitative data used in education and psychology are of a continuous nature. Examples of continuous variables, are muscular strength, anthropometric dimension intelligence. Body weight, height, chest measurement.

A discontinuous variable cannot be or is not generally, subdivided by less than whole number or units. Illustrations are basketball, scores numbers of pupil in a class room or children in a family school buildings and salary scales. Thus a basketball team could not score 471/2 points, the number of children in a family cannot be 21/3 school buildings are not counted until completely built and, although salary scales can be theoretically reduced to dollars and coins, in practice they are not. However, although fractions of such scores are unrealistic discontinuous details are frequently treated statistically is though they were continuous in order to provide significant differentiations. For example to state that the average number of children in families of two nationalities are 2.5 and 3.4 is to state the impossible yet no proper comparison recognize only whole number would round of the number child’s on the 3.0 for both nationalities