TESTING OF HYPOTHESIS(commerce)

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TOPIC TESTING OF HYPOTHESIS
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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.

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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.

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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

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Hypothesis

• 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

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Forms of Hypothesis

• 1. Descriptive Hypothesis
• 2. Relational Hypothesis or Explanatory
  Hypothesis
• 3. Null Hypothesis
• 4. Alternative Hypothesis

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Testing of Hypothesis

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

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TYPES

• State the hypotheses
• Formulate an analysis plan
• A. Significance level.
• B. Test Criterion.
• Analyze sample data
• Interpret results

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Decision Errors

• Type 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

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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

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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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Thanking you
By Maruthu pandi .M, Madurai, TN,Indian