The document discusses how to write a hypothesis for research. It explains that a hypothesis is a predicted relationship between two variables that can be tested. It recommends starting with a general hypothesis and refining it to be more specific and testable. The key components of a good hypothesis are that it must be measurable, specific enough to guide experiment design, and in an "if-then" format. The document also discusses how to write the null hypothesis, which assumes no relationship between variables, and the alternative hypothesis, which presents the predicted relationship. Overall, it provides guidance on formulating clear and testable hypotheses to lay the groundwork for effective research.

1. 3-11
Setting a Hypothesis
Dr. Shalini Sinha
Head of Department,
Department of Management Studies
NRI Institute of Information Science & Technology
Bhopal

2. 3-2

3. 3-3

4. 3-4

5. 3-5

6. 3-6

7. 3-7

8. 3-8

9. 3-9

10. 3-10
How to Write a Hypothesis
• Often, one of the trickiest parts of
designing and writing up any research
study is how to write a hypothesis.
• It is just about making sure that you are
asking the right questions and wording
your hypothesis statements correctly.

11. 3-11
The Three-Step Process
• Step one is to think of a general hypothesisgeneral hypothesis,
including everything that you have observedeverything that you have observed
and reviewedand reviewed during the information gatheringinformation gathering
stagestage of any research design.
• This stage is often called developing the researchdeveloping the research
problem.problem.

12. 3-12
Formulating a Hypothesis
• You have a question and now you need to
turn it into a hypothesis.
• A hypothesis is an educated prediction
that provides an explanation for an
observed event.

13. 3-13
• An observed event is a measurable result
or condition.
• If you can't measure it, then you can't
form a hypothesis about it because you
can't confirm or reject it.
• In addition, a hypothesis typically takes the
form of an if-then statement so you can test
it with your research.

14. 3-14
What does our hypothesis
look like?

15. 3-15
Observation
Bright light has an adverse
impact on studying.

16. 3-16
hypothesis
• What does our hypothesis look
like?
• 'If we increase the amount of light during
studying, then the participant's performance
on test scores will decrease.'

17. 3-17
Let's break down our hypothesis.
• First off, it is an if-then statement:
• 'If we increase..., then the participant's...’
• This creates a prediction that we can test by increasing
the light on participants as they study and then see if
their test scores go down.
• It also means that the hypothesis can be proven correct
or incorrect based on what happens to the test scores.
• If test scores don't change, then our hypothesis was
incorrect and we will reject it.

18. 3-18
• You probably also noticed that we changed 'studying' to
'test scores' and the vague term about 'bright light' into
'amount of light.'
• This is an example of operationalizingoperationalizing, which is finding a
way to measure or quantify a variable.
• Studying can't really be researched, but test scores can.
• And they are basically the same thing since studying
typically increases test scores.
• Also, simply saying 'light' is too vague to be useful or
researched, so it was turned into 'amount of light.
research hypothesis

19. 3-19
Null Hypothesis
• After you formulated your research hypothesis, what if
there isn't a connection between light and studying?
• That is kind of what a null hypothesis is;
• A null hypothesis is defined as a prediction that there
will be no effect observed during the study.
• The reason researchers develop a null hypothesis is to
ensure that their research can be proven
false.

20. 3-20
Null Hypothesis
• So whenever you are conducting a research with a
hypothesis, you will create a null hypothesis.
• Research typically includes a hypothesis, and
• when this is the case you will form a null hypothesis as a
counterbalancecounterbalance to ensure there is a way toto
disprove your prediction.disprove your prediction.

21. 3-21
Null Hypothesis
• Every hypothesis test contains a set of twoa set of two
opposing statementsopposing statements, or hypotheseshypotheses, about a
population parameterpopulation parameter.
• The first hypothesis is called the nullnull
hypothesis,hypothesis, denoted HH00..
• The null hypothesis always states that the
population parameter is population parameter is equalequal to the claimedto the claimed
value.value.
• FOR EGFOR EG. -GROWTH OF INDUSTRIAL OUTPUT BEFORE. -GROWTH OF INDUSTRIAL OUTPUT BEFORE
&AFTER ECONOMIC REFORMS IS THE SAME. (&AFTER ECONOMIC REFORMS IS THE SAME. ( NONO
CHANGE DUE TO REFORMS.CHANGE DUE TO REFORMS.)) BOTH ARE EQUALBOTH ARE EQUAL..

22. 3-22
• For example, if the claim is that the average time
to make a name-brand,MAGGI, ready-mix
noodles is two minutes, the statistical shorthand
notation for the null hypothesis in this case would
be as follows:
Ho : µ = 2
• (That is, the population mean is 2 minutes.)
• All null hypotheses include an equal sign in
them.

23. 3-23
Alternative Hypothesis
• How to define an alternative hypothesis ?
• Before actually conducting a hypothesis test, you
have to put two possible hypotheses on the table
— the null hypothesis is one of them.
• But, if the null hypothesis is rejected (that is,
there was sufficient evidence against it), what’s your
alternative going to be?

24. 3-24
Alternative hypothesis
• Actually, three possibilities exist for the second
(or alternative) hypothesis, denoted Ha. Here they
are, along with their shorthand notations in the
context of the Maggi noodles example:
1.1. The population parameter is The population parameter is not equalnot equal to the to the
claimed value.claimed value. Ha : µ ≠ 2
2.2. The population parameter is The population parameter is greater thangreater than thethe
claimed value.claimed value. Ha : µ ≥ 2
3.3. The population parameter is The population parameter is less thanless than the claimedthe claimed
value.value. Ha : µ ≤ 2

25. 3-25
Alternative hypothesis
• Which alternative hypothesis you choose
in setting up your hypothesis test depends
on what you’re interested in concluding,
should you have enough evidence to
refute the null hypothesis (the claim).
•

26. 3-26
• The alternative hypothesis should be
decided upon before collecting or looking
at any data, so as not to influence the
results.

27. 3-27
Alternative hypothesis
• For example, if you want to test whether a
company is correct in claiming its
noodles takes TWO minutes to make and
it doesn’t matter whether the actual
average time is more or less than that, you, you
use the not-equal-to alternativeuse the not-equal-to alternative.
• Your hypotheses for that test would be:
Ha : µ ≠2

28. 3-28
Alternative hypothesis
• If you only want to see whether the time
turns out to be greater than what the
company claims (that is, whether the
company is falsely advertising its quick
prep time), you use the greater-than
alternative, and your two hypotheses are
Ha : µ ≥ 2

29. 3-29
Alternative hypothesis
• Finally, say you work for the company
marketing the NOODLES, and you think
the NOODLES can be made in less than
five minutes (and could be marketed by the
company as such). The less-than alternative is
the one you want, and your two hypotheses
would be
Ha : µ ≤5

30. 3-30
Alternative hypothesis
• How do you know which hypothesis to put in
H0 and which one to put in Ha?

31. 3-31
H0
• Typically, the null hypothesis says that
nothing new is happening;
• the previous result is the same now as it
was before,
• or the groups have the same average (their
difference is equal to zero).

32. 3-32
• In general, you assume that people’s claims
are true until proven otherwise.
• So the question becomes: Can you prove
otherwise?
• In other words, can you show sufficient
evidence to reject H0?

33. 3-33
Another Example of How to Write a Hypothesis
• A worker on a fish-farm notices that his trout
seem to have more Anchor worms in the
summer, when the water levels are low,
and wants to find out why.
• His research leads him to believe that the
amount of oxygen is the reason - fish that
are oxygen stressed tend to be more
susceptible to disease and parasites.

34. 3-34
• He proposes a general hypothesis.
• “Water levels affect the amount of Anchor
worms suffered by rainbow trout.”
• This is a good general hypothesis, but it
gives no guide to how to design
the research.
• The hypothesis must be refined to give ahypothesis must be refined to give a
little directionlittle direction..

35. 3-35
• “Rainbow trout suffer more worms when
water levels are low.”
• Now there is some directionality,
• but the hypothesis is not really testable, s
• o the final stage is to design an
experiment around which research can be
designed, a testable hypothesis.

36. 3-36
• Rainbow trout suffer more worms in low
water conditions because there is less
oxygen in the water.”
• This is a testable hypothesis - he has
established variablesestablished variables, and by measuring the
amount of oxygenamount of oxygen in the water.
• He can see if there is a correlation against
the number of wormsnumber of worms on the fish.

37. 3-37
• This is an example of how a graduala gradual
focusing of researchfocusing of research helps to define how to
write a hypothesis.

38. 3-38
• The null hypothesis is essentially the
"devil's advocate" position.
• That is, it assumes that whatever you are
trying to prove did not happen (hint: it
usually states that something equals zero).
For example, the two different teaching
methods did not result in different exam
performances (i.e., zero difference).

39. 3-39
• Another example might be that there is nothere is no
relationship between anxiety and athleticrelationship between anxiety and athletic
performance (i.e., the slope is zero).performance (i.e., the slope is zero).

40. 3-40
Alternative hypothesis
• The alternative hypothesis states the
opposite of Ho and is usually the
hypothesis you are trying to prove
• (e.g., the two different teaching methods
did result in different exam
performances).

41. 3-41
• Initially, you can state these hypotheses in
more general terms (e.g., using terms like
"effect", "relationship", etc.), as shown below
for the teaching methods example:
• Null Hypothesis (H0):Undertaking
seminar classes has no effect on students'
performance.
• Alternative Hypothesis (HA):Undertaking
seminar class has a positive effect on
students' performance.

42. 3-42
The Next Stage - What to Do With the
Hypothesis
• Once you have your hypothesis
• the next stage is to design the research,
• allowing a statistical analysis of data, and
allowing you to test your hypothesis.

43. 3-43
• The statistical analysis will allow you to
reject either the null or the alternative
hypothesis.
• If the alternative is rejected, then you
need to go back and refine the initial
hypothesis or design a completely new
research program.

44. 3-44
A rule of the thumb
• A rule of the thumb is to set the Null-
Hypothesis to the outcome you do not
want to be true i.e. the outcome whose
direct opposite you want to show.

45. 3-45
• Basic example: Suppose you have developed a new
medical treatment and you want to show that it is
indeed better than placebo.
• Placebo.
• a medicine or procedure prescribed for the
psychological benefit to the patient rather than for
any physiological effect.
• a substance that has no therapeutic effect, used as a
control in testing new drugs
•

46. 3-46
• So you set Null-Hypothesis H0:=new
treament is equal or worse than
placebo and Alternative
Hypothesis H1:=new treatment is better
than placebo.
• This because in the course of a statistical test
you either reject the Null-Hypothesis (and
favor the Alternative Hypothesis) or you
cannot reject it.
• Since your "goal" is to reject the Null-
Hypothesis you set it to the outcome you
do not want to be true.

47. 3-47
• THANKS !