TOPIC :TESTING OF HYPOTHESIS

Introduction Testing of hypothesis is one of the most important

tools of application of statistics to real life problems.

The testing of hypothesis is an important step in the process of research and a prerequisite of any successful research work as it helps a researcher to get rid of vague approaches and meaningful interpretations.

Variables and Attributes

A variable is a characteristic that takes on two or more values whereas, an attribute is a specific value on a variable (qualitative).

Ex : the variable ‘Gender’ has two attributes

namely male and female. Similarly, another variable ‘Agreement’ has five attributes, namely, Strongly Agree, Agree, Neutral, Disagree and Strongly Disagree.

Variables and Attributes Types

1. Explanatory and Extraneous Variables 2Dependent and Independent Variable 3. Categorical Variables a. Nominal Variables b. Ordinal Variables C. Dichotomous Variables

4.Continuous Variables a. Interval variables

b. Ratio variables

A hypothesis is a specific conjecture (statement) about a property of a population of interest. It is a logically conjectured relationship between two or more variables expressed in the form of a testable statement. In other words, it is a predictive statement that relates an independent variable to a dependent variable. Every hypothesis must contain at least one independent variable and one dependent variable

Hypothesis

Forms of Hypothesis1. Descriptive Hypothesis

2. Relational Hypothesis or Explanatory Hypothesis

3. Null Hypothesis

4. Alternative Hypothesis

Testing of HypothesisThere are three approaches to testing of hypothesis. Each approach requires different subjective criteria and objective statistics but ends up with the same conclusion.

TYPESState the hypotheses

Formulate an analysis plan A. Significance level.

B. Test Criterion.

Analyze sample data

Interpret results

Decision ErrorsType I error. A Type I error occurs when

the researcher rejects a null hypothesis when it is true. The probability of committing a Type I error is called the significance level.

Type II error. A Type II error occurs when the researcher fails to reject a null hypothesis that is false. The probability of committing a Type II error is called Beta

Decision Rules P-value. The strength of evidence in

support of a null hypothesis is measured by the P-value

Region of acceptance. The region of acceptance is a range of values. If the test statistic falls within the region of acceptance, the null hypothesis is not rejected

Conclusion A hypothesis is a tentative generalization the

validity of which remains to be seen. The task of deriving a suitable hypothesis is essentially parallel to that of selection of a suitable research problem. The testing of hypothesis provides direction to the researcher by suggesting how to proceed further in the process of discovering new facts. The testing of hypothesis can be carried out on one or two samples using appropriate statistical tools and techniques.

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