Upload
maruthupandi-m
View
11
Download
1
Embed Size (px)
Citation preview
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.
Thanking you