This document discusses hypothesis construction and testing in statistical analysis. It defines key terms like the null and alternative hypotheses. The null hypothesis states that there is no effect or difference, while the alternative explores the research question. Hypotheses must be clear, testable, specify variable relationships, be limited in scope, stated simply, and consistent with known facts. The process of hypothesis testing involves setting up the hypotheses and significance level, determining a suitable test, identifying the critical region, performing computations, and making a decision. Hypotheses are not always necessary but can add clarity to research findings. The document also contrasts inductive and deductive approaches to research.

1. Constructing Hypothesis

2. Statistical inference. Role of chance.
R e a s o n a n d i n t u it i o n E m p ir i c a l o b s e r v a t i o n
S c ie n t i f i c k n o w le d g e
Formulate
hypotheses
Collect data to
test hypotheses

3. Statistical inference. Role of chance.
Formulate
hypotheses
Collect data to
test hypotheses
Accept hypothesis Reject hypothesis
C H A N C E
Random error (chance) can be controlled by statistical significance
or by confidence interval
Systematic error

4. Hypothesis Overview
• Oversimplified or incorrect assumptions must
be subjected to more formal hypothesis
testing. Example:
• Bankers assumed high-income earners are more profitable
than low-income earners
• Clients who carefully balance their checkbooks every month
and minimize fees due to overdrafts are unprofitable
checking account customers
• Old clients were more likely to diminish CD balances by large
amounts compared to younger clients
– This was non-intutive because conventional wisdom suggested
that older clients have a larger portfolio of assets and seek less
risky investments

5. Characteristics of Hypothesis
1. Clear and precise
2. Capable of being tested
3. State relationship between variables
4. Limited scope and must be specific
5. Stated in simple terms
6. Consistent with most known facts

6. Procedure of Hypothesis testing
1. Set up a hypothesis
2. Set up a suitable significance level
3. Determination of suitable test statistics
4. Determine the critical region
5. Doing computations
6. Making decisions

7. Types of Hypothesis
The two hypotheses are called the null hypothesis and
the other the alternative or research hypothesis. The
usual notation is:
H0: — the ‘null’ hypothesis
H1: — the ‘alternative’ or ‘research’ hypothesis
The null hypothesis (H0) will always state that the
parameter equals the value specified in the
alternative hypothesis (H1)
pronounced
H “nought”

8. Testing of hypotheses
Null hypothesis H00 -- there is no difference
Alternative hypothesis HAA - question explored
by the investigator
Statistical method are used to test hypotheses
The null hypothesis is the basis for statistical

9. Are hypothesis always necessary?
• Whether their acceptance or
rejection will help in accomplishing
objectives of research.
• It only adds clarity to the findings.

10. Inductive Research Approach
• Inductive reasoning works
the other way, moving
from specific observations
to broader generalizations
and theories.
• Informally,, we
sometimes call this a
"bottom up“ approach
• Conclusion is likely based
on premises.
• Involves a degree off
uncertainty
ObservationsObservations
PATTERNPATTERN
TENTATIVE
HYPOTHESIS
TENTATIVE
HYPOTHESIS
THEORYTHEORY
Hill
Climbing

11. Deductive Research Approach
• Deductive reasoning
works from the more
general to the more
specific.
• Sometimes this is
informally called a
"top-down“ approach.
• Conclusion follows
logically from premises
(available facts)
THEORYTHEORY
HYPOTHESISHYPOTHESIS
OBSERVATIONOBSERVATION
CONFIRMATIONCONFIRMATION
Waterfall

12. Thanks!