what is hypothesis?

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Hypothesis
Hypothesis is
• An educated guess
• A claim or statement about a property of a population
Definition
[What is hypothesis? (BSMMU, January, 2009)]
Hypothesis is a conclusion drawn before all the facts are
established and tentatively accepted as a basis for further
investigation.
Characteristics of Hypothesis
[What are the important features of a research hypothesis?
(BSMMU, 2009)]
A hypothesis must possess the following characteristics:
1. Hypothesis should be clear and precise.
2. Hypothesis should be capable of being tested. A hypothesis
“is testable if other inferences can be made from it which, in
turn, can be confirmed or disproved by observation.”
3. Hypothesis should state relationship between variables, if it
happens to be a relational hypothesis
4. Hypothesis should be limited in scope and must be specific.
A researcher must remember that narrower hypotheses are
generally more testable and he should develop such
hypotheses
5. Researchers should state hypothesis as far as possible in
most simple terms.

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6. Hypothesis should be consistent with most known facts i e.,
it must be consistent with a substantial body of established
facts. In other words, it should be one which judges accept
as being the most likely
7. Hypothesis should be amenable to testing within a
reasonable time. One should not use even an excellent
hypothesis, if the same cannot be tested in reasonable time
for one cannot spend a life-time collecting data to test it
8. Hypothesis must explain the facts that gave rise to the need
for explanation. This means that by using the hypothesis plus
other known and accepted generalizations, one should be
able to deduce the original problem condition. Thus
hypothesis must actually explain what it claims to explain; it
should have empirical reference.
Steps of Hypothesis Testing
[Discuss the steps of hypothesis testing. (BSMMU, January, 2009)]
1. The first step is to specify the null hypothesis. A typical null
hypothesis is equivalent to μ1 = μ2.
2. The second step is to specify the α level which is also known
as the significance level. Typical values are 0.05 and 0.01.
3. The third step is to compute the probability value (also
known as the p value).
4. Finally, compare the probability value with the α level. If the
probability value is lower then you reject the null hypothesis.
Keep in mind that rejecting the null hypothesis is not an all-
or-none decision. The lower the probability value, the more

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confidence you can have that the null hypothesis is false.
However, if your probability value is higher than the
conventional α level of 0.05, most scientists will consider
your findings inconclusive. Failure to reject the null
hypothesis does not constitute support for the null
hypothesis. It just means you do not have sufficiently strong
data to reject it.
Limitations of null hypothesis tests
 Hypothesis tests do not relate to the main question of
interest (whether or not there is a true difference in the
population), and only provide degrees of evidence in favor or
against there being no true difference.
 Another limitation is that there will always be a difference of
some magnitude between the two groups, even if this is of
no relevance. Consider a cohort study where 1 million non-
diseased individuals are followed up to see whether or not
exposure to substance x is associated with disease. It may be
that in this whole population of 1 million animals, 10.0% of
exposed individuals develop the disease and that 9.9% of
unexposed individuals develop the disease. Of course, this
difference is not of any biological relevance, and yet there is
a difference there (as this is a whole population rather than a
sample, we would not conduct a hypothesis test). As the size
of any sample increases, the ability to detect a true
difference increases. As there will be a 'true difference'
(however small) in most populations, this means that
hypothesis tests on large sample sizes will tend to give low
p-values (indeed, some statisticians view hypothesis testing
as a method of determining whether or not the sample size
is sufficient to detect a difference). This problem can be

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reduced by ensuring that the appropriate measure of effect
is always presented along with the hypothesis test p-value. In
the example above, the incidence risk of disease amongst
exposed individuals was 0.100, and that amongst unexposed
was 0.099, giving a risk ratio of 0.100/0.099 = 1.01. Therefore,
regardless of the result of hypothesis testing, there is very
little association between exposure and disease in this case.
Disadvantages of Hypothesis Testing
1. Dependent on concentrations tested.
2. Statistical power is influenced by variability.
3. Inability to calculate confidence intervals.
4. Confounded by poorly behaved data.
5. Frequently need to use non-parametric statistical methods.