The document outlines the concepts and distinctions between hypotheses and assumptions in research, detailing various types of each and their significance in decision-making processes. It explains the definition, importance, characteristics, and testing of hypotheses, alongside the foundational role of assumptions in guiding research. Additionally, it discusses the decision-making steps in biostatistics, highlighting methods of data collection and analysis, and challenges faced in making informed decisions.

1. HYPOTHESIS ASSUMPTION AND
DECISION MAKING
Guided By:-
Dr. NAVEEN KUMAR
VISHWAKARMA
Department of
Biotechnology,
Presented By:-
Firdous Ashraf
Pratibha Jha
Priyanka Munda
Khusboo Mahesh
Archie Kurrey

2. CONTENT-
HYPOTHESIS
TYPES OF HYPOTHESIS
ASSUMPTION
TYPES OF ASSUMPTION
DIFFERENCE BETWEEN HYPOTHESIS AND ASSUMPTION
DECISION MAKING

3. Hypothesis

4. Origin of hypothesis
• The word hypothesis is derived from the Greek word - 'hypotithenai' .
• The word hypothesis consists of two words 'Hypo' and 'thesis'.
• 'Hypo' means to put under or suppose.
• 'Thesis' means placing or proposal .
• So the word "Hypothesis" means the guesses to solve the research
problem.

5. Definition of Hypothesis
• "It is a tentative supposition or provisional guess which seems
to explain the situation under observation." - James E.
Greighton
• Hypothesis is a formal statement that presents the expected
relationship between an independent and dependent variable.
(Creswell,1994)
• Hypothesis is stated before the data collection.
• It is the powerful tool in research process (helps researcher to
relate theory to observation)

6. Importance of a hypothesis
• To the point enquiry
• Separating Relevant from Irrelevant observation
• Selecting required facts
• Direction of research
• Prevent blind research
• Provide answer for question
• It provide link between theories and actual practical
• Save money ,time ,energy
• Proper data collection
• Proper conclusion

7. CHARACTERISTICS OF A GOOD
HYPOTHESIS
• Conceptual clarity & availability of techniques
• Empirical referent & economical
• Objectivity & consistency
• Specificity & simplicity
• Relevant & purposiveness
• Testability & verifiability

8. SOURCES OF HYPOTHESIS
• Experience of researcher
• Review of literature
• Interaction with knowledgeable persons
• Knowledge of culture and society
• Creative thinking and imagination of researcher
• Observation

9. Types of Hypothesis
• Simple Hypothesis
• Complex Hypothesis
• Empirical Hypothesis
• Null Hypothesis
• Alternative Hypothesis
• Logical Hypothesis
• Statistical Hypothesis

10. Types of Hypothesis
 Simple Hypothesis : Simple hypothesis is that
one in which there exists relationship between
two variables one is called independent variable
or cause and the other is dependent variable or
effect. For ex : If
you eat more vegetables , you will lose weight
faster
 Complex hypothesis : A complex hypothesis
predicts the relationship between two or more
independent and dependent variables. For
ex : Eating more vegetables and fruits will lead to
loss in your weight

11.  Empirical Hypothesis : An empirical
hypothesis is a theory that is based on
observations or experiments from the
past. It is also referred to as a working
hypothesis.

12.  Null Hypothesis : The null hypothesis in
statistics states that there is no difference
between groups or no relationship between
variables. It is one of two mutually exclusive
hypotheses about a population in a hypothesis
test. For
example : When your sample contains sufficient
evidence, you can reject the null and conclude
that the effect is statistically significant.

13.  Alternative Hypothesis : This is also known as
the claim. This hypothesis should state what you
expect the data to show, based on your research
on the topic. This is your answer to your
research question.
 Logical Hypothesis : A logical hypothesis is a
hypothesis that cannot be tested, but has some
logical basis underpinning our assumptions.
For ex : Dinosaurs
closely related to Aligators probably had green
scales because Aligators have green scales.
However, as they are all extinct, we can only rely
on logic and not empirical data.

14.  Statistical Hypothesis : A statistical hypothesis
utilizes representative statistical models to draw
conclusions about broader populations.
For ex : Human Sex Ratio.
The most famous statistical hypothesis example
is that of John Arbuthnot’s sex at birth case
study in 1710. Arbuthnot used birth data to
determine with high statistical probability that
there are more male births than female births.
He called this divine providence, and to this day,
his findings remain true: more men are born
than women.

15. Hypothesis Testing
In hypothesis testing,we decide whether to reject or
accept a particular value of a set of particular values
of a parameter or those of several values .It is seen
that , although the exact value of a parameter may be
unknown ,there is often same idea about the true
value.The data collected from the sample helps us in
rejecting or accepting our hypothesis.In dealing with
problems of Hypothesis Testing ,we try to arrive at the
right decision about the pre stated hypothesis .

18. ASSUMPTION

19. Origin Of Assumption

20. ASSUMPTIONS
Assumptions are statements that are taken for granted or
are considered true, even though they have not been
scientifically tested.Assumptions are principles those are
accepted as being true based on logic or reasons but
without proof or verification.

21. IMPORTANCE OF ASSUMPTIONS IN RESEARCH
Assumptions work as a foundation or base for
researcher.
Selection of research topic can be based on written
assumption or assumptions of the previous studies.
Assumptions helps in research process and conclude
the research study.
 Verified and tested assumptions expand the body of
knowledge.

22. TYPES OF ASSUMPTION
Universal Assumption
Based on theories
Empirical based assumption
Research/ methodology Assumption
Warranted
Unwarranted

23. 1.UNIVERSAL ASSUMPTIONS
These are beliefs that assumed to be true by a large part of
society or universe. Testing of such assumption is not always
possible
EXAMPLE; Divine power controls the universe.

24. 2.ASSUMPTIONS BASED ON THEORIES
It may also be drawn from theories. If a research study is based on
a theory, the assumption of particular theory may become
assumption of that particular research study.
EXAMPLE; Physics assumptions, In physics, scientists assume that
the gravitational forces on a falling ball are related to those on
other falling objects. They also assume that gravity works the same
way every time, regardless of the object or the moment.

25. 3.EMPIRICAL BASED ASSUMPTIONS
These are derived from previous research studies. They are therefore
considered the most reliable.
EXAMPLE; Use of drug warfarin for ischemic heart disease or coronary
artery bypass grafting surgery patients improves blood circulation and
prevents thrombus formation.
4.RESEARCH / METHODOLOGICAL ASSUMPTIONS
Researcher has to formulate some of the methodological assumptions to
conduct a research study.
EXAMPLE; Participants will be participating in the study willingly and
respond to the research tools honestly.

26. 5.WARRANTED ASSUMPTIONS
These type of assumptions stated along with the proof or evidences to
support.
EXAMPLE; Breakfast before school, the assumption that children who eat
breakfast will be able to concentrate and complete tasks.
6.UNWARRANTED ASSUMPTIONS
These assumptions do not have supportive evidence or proof. Sometimes,
it will be difficult to fulfill these kind of assumptions.
EXAMPLE; Biases, Believing that someone who dresses casually at a
professional event is not successful or serious about their caree

27. HYPOTHESIS vs ASSUMPTION
ASPECTS HYPOTHESIS ASSUMPTION
Definition A testable statement or claim about a
population parameter that can be
evaluated using statistical methods.
A statement or condition accepted as
true without proof, assumed for the
purpose of analysis.
Purpose Hypothesis are statements that are
tested using data to make inference
about a population.
Assumptions provide the foundation or
starting point for statistical methods,
models and tests.
Nature Hypothesis are specific, testable
prediction or claims that can be
supported or rejected based on sample
data.
Assumption are typically general
condition that are taken as given in the
model (e.g., normality, independence).
Example The mean of the population is equal to a
specific value.
-There is a relationship between two
variables.
Data is normally distributed.
-Observations are independent.
-Variance is constant (homoscedasticity).

28. ASPECTS HYPOTHESIS ASSUMPTION
Testing / Verification Hypothesis are directly tested using
statistical tests(e.g., t-test, chi-squared
test) to determine if they can be
accepted or rejected.
Assumption are not directly tested in
hypothesis testing but may affect the
validity of the test. Some assumption
can be tested indirectly (e.g.,
normality).
Role in analysis Hypothesis define the research
question and determine the outcomes
of the statistical analysis (accept or
reject).
Assumptions define the model
framework and the conditions under
which the analysis is valid.
Example in context Null hypothesis: The average income of
the population is $50,000.
- Alternative hypothesis: The average
income of the population is not
$50,000.
The data follows a normal distribution
(assumption for many parametric
tests).
- The sample is random (assumption
for unbiased estimates).

29. DECISION MAKING
Decision making is the process of identifying and
choosing between alternatives to achieve a specific
objective or solve a problem. It involves analyzing
available information, considering possible outcomes,
and selecting the most appropriate course of action.

30. Decision-Making Process in Biostatistics
In biostatistical decision making, the process typically involves the following
steps:
Step 1: Problem Formulation
The first step is to define the problem clearly. For example:
•Epidemiological studies: What are the risk factors for a particular disease?
Step 2: Data Collection
Data can come from various sources:
•Epidemiological studies: Cohort studies, case-control studies, cross-sectional
studies.
•Public health data: Surveys, national health data.

31. Step 3: Data Analysis
Biostatistical analysis tools are used to analyze the data.
Common methods include:
•Hypothesis Testing: To assess if there is sufficient evidence to
support or reject a hypothesis (e.g., t-tests, chi-square tests,
ANOVA).
•Regression Analysis: To explore relationships between
variables (e.g., logistic regression for binary outcomes).
•Bayesian Analysis: To incorporate prior knowledge and update
beliefs as new data are collected.

32. Step 4: Decision Criteria
Decision criteria are established to guide the decision-making
process. These could include:
•Confidence Intervals: Used to estimate the range of plausible
values for a parameter, providing a sense of uncertainty around
the estimate.
•Effect Size: The magnitude of the treatment effect, which helps
to interpret the practical significance of results.
Step 5: Make the Decision
Based on the analysis, the biostatistician makes a decision.
For example- in clinical trials, this could be whether a drug is
safe and effective, or in epidemiology, whether a risk factor
significantly influences disease occurrence.

33. •Uncertainty: Data is often imperfect, and decisions must be made under
uncertainty. Decisions can be influenced by sampling variability,
measurement errors, or confounding factors.
•Ethical Issues: Especially in clinical trials, there may be ethical dilemmas in
making decisions that affect patient outcomes, such as determining
whether a treatment should be administered to certain individuals.
•Data Quality: Incomplete, biased, or misreported data can significantly
affect decision-making.
•Risk Management: In healthcare and public health, decisions often involve
balancing risks, such as false positives and false negatives in diagnostic
tests, or underestimating the side effects of treatments.
Challenges in Decision Making in Biostatistics

34. Reference
1) P. N. Arora and P. K. Malhan, Biostatistics, (Latest ed)
Himalaya PublishingHouse.
2) Biostatistics: a Foundation for Analysis in the Health
Sciences, (Latest ed)., Wiley.
3) 3. P. N. Arora and P. K. Malhan, Biostatistics, (Latest
ed). Himalaya PublishingHouse.

35. THANKYOU