1. Research Methods in MIS
Hypothesis, Propositions, and Hypothesis Testing
Dr. Deepak Khazanchi

2. Steps in The Research Process
Review Research Ethics
Hypothesis Development
Vs. Research Questions
Testing basics

3. Research Questions and Hypothesis
Research Question: an interrogative statement asking about a conjectured relationship between two or more variables.
Is the use of Internet Commerce positively related to sales volume in the furniture industry?
Will attitudes towards computer usage be better for employees receiving training.
Hypothesis: A declarative statement indicating a conjectured relationship between two or more variables which can be tested.
Employees receiving training will have positive attitudes towards computer usage.

4. Do all research projects require Hypothesis testing?
Definitely NOT!
Many projects use research questions.
Research questions vs. Hypotheses
Hypotheses state predicted relationships between variables
Research questions ask if a relationship exists
Form of each is the same, except RQ is interrogative and the hypothesis is a statement.
Research process is similar in almost all respects and most steps are identical
Research projects can include both research questions and hypotheses.

5. Hypothesis vs. Research Question
Use Hypothesis Testing if:
Sufficient information about the variables and their relationship can be found in the review of related information to formulate an hypothesis.
Samples, not populations, are used.
You wish to use hypothesis testing procedures
Use Research Questions if:
There is insufficient information available to formulate hypothesis
Population, not samples, are used.
You are not willing to pre-specify a probability level at which the hypothesis will be considered supported.
You do not wish to use hypothesis testing procedures.

6. The Role of the Hypothesis
Guides the direction of the study
Identifies facts that are relevant
Suggests which form of research design is appropriate
Provides a framework for organizing the conclusions that result

7. What is a Good Hypothesis?
A good hypothesis should fulfill three conditions:
Must be adequate for its purpose
Must be testable
Must be better than its rivals

8. Hypothesis Testing
Process of making statistical inferences about population characteristics by using data obtained from samples of that population.
Research Hypothesis: A declarative statement indicating conjectured relationship between variables which can be tested and which the researcher believes will be demonstrated after testing.
Also called working hypothesis, alternate hypothesis, motivated hypothesis.
Symbolically represented as H1: E = N

9. Null vs. Research Hypothesis
Research hypothesis is not the one tested. It is the Null hypothesis that is tested.
Null Hypothesis is a declarative statement indicating that the relationship specified in the research hypothesis will not exist.
Symbol: H0: E > N
State the research hypothesis, not he null hypothesis (despite what you may have learned in statistics class).
The research hypothesis is the one in which you are interested.
The null hypothesis is always the one which will tested.

10. Directional/Nondirectional Hypothesis
Directional: An hypothesis in which the direction of the outcome is predicted rather than predicting inequality only.
E.g., A will be better than B, X will be less than Y, A will be positively related to B.
NonDirectional: An hypothesis in which the direction of the outcome is not predicted and the states relationship between the variables is one of inequality.
E.g., A will be unequal to B, A will be related to B.

11. Probability
Probability: The relative frequency of an event occurring, usually reported as a percentage or fraction.
The probability of drawing an ace of hearts from a deck of 52 cards is 1/52 or 0.19.
Probabilities range from 0 to 1 with 0 indicating no chance of occurrence an d1 indicating 100% chance or certainty that an event will occur.
A probability of 0.05 means that an event should occur 5 times out of every t100 times the same situation takes place.

12. Significance Level
Researchers use probability to specify the risk (probability) of coming to the wrong conclusions in hypothesis testing.
They specify a probability of being wrong such as 0.05 or 0.01 which means that they want to draw the correct conclusion 95 or 99 times out of 100.
The 0.05 or 0.01 value is called significance level. It is the probability, expressed in a percentage, of rejecting the null hypothesis when it is true and should not have been rejected.
Also known as TYPE I Error, ALPHA LEVEL, ALPHA ERROR
Always selected a priori

13. TYPE I and TYPE II Errors
Type I Error (Alpha, ): Probability of REJECTING THE NULL HYPOTHESIS WHEN IT IS TRUE.
TYPE II Error (Beta, ): Probability of FAILING TO REJECT (ACCEPTING) THE NULL HYPOTHESIS WHEN IT IS FALSE.
Ideal goal: Reduce both types of errors.
Not feasible, since smaller the TYPE I error means larger the TYPE II error will be.
Generally decision maker must assess which error is more important.

14. POWER
Power is the probability, expressed in a percentage, of rejecting the null hypothesis when it is false and should have been rejected; a correct decision.
Power is computed as 1.00-beta (Type II error).

15. POSSIBLE RESULTS OF HYPOTHESIS TESTING
FAIL TO REJECT (ACCEPT) H0
REJECT H0
(ACCEPT HA)
H0 IS TRUE
CORRECT DECISION
(Prob. of accepting H0 when it is true = 1-alpha)
TYPE I ERROR
(Prob. of rejecting H0 when it is true = alpha)
H0 IS FALSE
TYPE II ERROR
(Prob. of accepting H0 when it is false =beta)
(Prob. of rejecting H0 when it is false = power
= 1-beta)

16. Reducing TYPE II Error (Beta)
Increase the significance level (alpha)
Increases the risk of rejecting the null, when it is true (Type I error)
Increase the size of the sample
Can be costly and time consuming.
Lowering Type II error increases the power of the statistical test you use.