WAYS OF STATING A HYPOTHESIS

• A hypothesis is a testable statement of the relationship between the variables under study.
• Since it is testable, it can be shown whether a hypothesis is either true or false.
• When we are aware of these characteristics of a hypothesis, we must know how to state a
hypothesis.
• According to the suggestion given by L Russell (see Reichenbach, 1947) the hypothesis
should be stated in the logical form of the general implication.
• A hypothesis in form of a general implication may be expressed in terms of an "lf... then.."
relationship or "If a then b'. In other words, if a condition holds true then some other
conditions also hold true.
• For example, if the reward is given for learning a task then the learning is improved.
• In general implication the a condition is referred to as the antecedent condition and the b
condition is referred to as the consequent condition.
• The antecedent condition as well as the consequent condition is expressed in the form of
propositions and therefore, the hypothesis expresses the relationship between the above
two types of propositional statements.
• In stating the hypothesis as general implications there occur two common
misunderstandings regarding the antecedent condition and the consequent condition.
• First, it is generally misunderstood that the antecedent condition causes the consequent
condition. This is not always true. The general implication only says that if the hypothesis is
probably true then the antecedent condition and the consequent condition will occur
together and not that one causes the other.
• Second, it is commonly misunderstood that the consequent conditions are always true, But
in reality the general implication does not say so. It only states that if the antecedent
conditions are true then the consequent conditions are also true.
• Now the question is: Are we following Russell's standard general implication in stating the
hypothesis?
• The answer is probably `no'. Recent publications of scientists have clearly shown that they
are not following the "lf the hypothesis in the form of a statement, which is direct and does
not contain the "If... then ..“ form.
• For example: (i) Reward improves learning. (i) The onset of fatigue reduces the efficiency of
workers.
• If we are not conforming to the advice of Russell, we are not necessarily committing a
serious error in stating the hypothesis because the above type of hypothesis can be
restated in the "If ..., then." form of Russell also.
• For example, "If a reward is given, learning is improved" and "if fatigue occurs, the
efficiency of the worker is reduced."
• It is obvious that the hypothesis can be formulated in different ways.
TYPES OF HYPOTHESES
• On the basis of the degree of generality, research hypothesis can be divided into two types:
(a) Universal hypothesis (b) Existential hypothesis
• A universal hypothesis is one in which the stated relationship holds good for all the levels or
values of variables which are specified for all time at all places.
• `Adequate level of light increase reading efficiency' is an example of universal hypothesis.
• Existential hypothesis is one which states that the relationship stated holds good for at least
one particular case.
• For example, there is at least one schizophrenic who does not have either delusion or
hallucination' is an example of existential hypothesis.
• Of these two types of hypotheses, the universal hypothesis is preferred because such a
hypothesis has a greater predicative power than the existential hypothesis.
• As we know, hypothesis is a formally stated expectation about a behaviour that defines the
purpose and goals of the study being conducted.
• Based upon the goals of explaining and controlling the causes of behaviour, there are two
types of hypothesis: 1. Causal hypothesis 2. Descriptive hypothesis
• A causal hypothesis postulates a particular causal influence or behaviour. In other words. it
tentatively explains a particular influence on, or a cause for, a particular behaviour.
• For example, if the researcher hypothesizes that boring contents of commercial
advertisements is the cause of channel changing by TV viewers it becomes the example of
causal hypothesis.
• Although it is a fact that boring contents may not be the only causal influence of the
channel-changing behaviour, it is the probable cause we are investigating at the moment.
• Descriptive hypothesis is one that postulates particular characteristics of a behaviour or
provides some specific goal for the observation.
• In fact, such hypothesis tentatively describes a behaviour in terms of the characteristics or
the situation in which it occurs.
• Such hypothesis identifies the various characteristics or attributes of behaviour and allows
us to predict when it occurs.
• For example, if the researcher hypothesizes that channel changing during TV viewing
occurs more frequently when the person is alone than when he is watching with others.
• The reality may be that even the number of people present might partially cause channel
changing and the researcher has not stated that.
• In this way, it can be said that a descriptive hypothesis simply describes the behaviour in
terms of the various characteristics of the situation and it does not attempt to identify the
causes of a behaviour.
• Apart from these, the other type of hypotheses that we commonly use in behavioral
researches are simple hypothesis, complex hypothesis, research hypothesis, null
hypothesis and statistical hypothesis.
• These may be described as under:
• 3. Simple hypothesis: Simple hypothesis contains only one or two variables.
• For example, hypotheses like children from broken homes tend to become delinquent,
reward improve learning, aggression is associated with frustration are all examples of
simple hypotheses.
• In all these hypotheses the relationship between only two variables have been postulated.
Hence, they are examples of simple hypotheses.
• 4. Complex hypothesis: Complex hypotheses are hypotheses which contain more than two
complex because the interrelatedness of more than two variables acting simultaneously is more
variables and therefore, require complex statistical calculation too.
• Such hypotheses are called difficult to assess quantitatively and theoretically.
• A hypothesis like children from upper and lower socio-economic status have larger adult
adjustment problems than children from middle socioeconomic status is an example of a
relatively complex hypotheses.
• 5. Research hypothesis: A hypothesis derived from the researcher's theory about some
aspects of behaviour is called a research hypothesis or is also known as a working
hypothesis.
• The researcher believes that his research hypotheses are true or that they are accurate
statements about the conditions of things he is investigating.
• He also believes that these hypotheses are true to the extent that the theory from which
they were derived is adequate. In this perspective, Siegel and Castell (1988) have defined
research hypothesis as, "the prediction derived from the theory under test."
• 6. Null hypothesis: A null hypothesis (H,) is, in a sense, the reverse of a research
hypothesis. It is, in fact, a no-effect or difference hypothesis or negation hypothesis that
tends to refuse or deny what is explicitly indicated in a given research hypothesis.
• Generally, the experimenter or researcher's aim is to refuse this hypothesis on the basis of
the obtained results so that its reverse, that is, the research hypothesis can be supported
or confirmed.
• 7. Statistical hypothesis: A statistical hypothesis, also known as alternative hypothesis (H),
is one that makes numerical expressions of null hypotheses and of research hypotheses. In
other words, it is the operational statement of the investigator's research hypothesis.
• The interrelatedness of research hypothesis, null hypothesis and alternative, or statistical,
hypothesis can be explained through an example:
• Suppose a certain social-psychological theory would lead us to predict that two specified
groups of people would differ on the measure of intelligence.
• This prediction would be our research hypothesis which would state that the two groups
differ.
• Confirmation of this hypothesis would lend support to the theory from which it was derived.
• To test this research hypothesis, we state it in an operational form as the alternative
hypothesis, that is, H,.
• One operational way to state this alternative hypothesis would be that mean intelligence
scores of these two groups differ or are unequal.
• The null hypothesis (H, ) would be that the mean intelligence score of the two groups is the
same.
• If the data permits us to reject H, then H, would be accepted because the data support the
research hypothesis and its underlying theory.
• In fact the nature of the research hypothesis determines how the alternative hypothesis (H,)
should be stated.
• If the research hypothesis simply states that two groups will differ with respect mean then
the alternative hypothesis would be simply that mean of the two groups are not equal.
• But if the research hypothesis predicts difference with direction, that is, one specified group
will have a larger mean than the other then the alternative hypothesis may be that the
mean of group 1 is greater or less than the mean for group 2.
 Errors in Testing of Hypothesis:
• There are basically two types of errors we make in the context of testing of Hypothesis.
• These are called as Type-I error and the Type-II error.
• In type-I error, we may reject Null hypothesis when Null hypothesis is true.
• Type-II error is when we accept Null hypothesis when the Null Hypothesis is not true.
• In other words, Type-I error means rejection of hypothesis which should have been
accepted and Type-II error means accepting the hypothesis which should have been
rejected.
• Type-I error is denoted by alpha known as alpha error, also called the level of significance
of test and Type-II error is denoted by beta known as beta error.
 Accept Null hypothesis Reject Null hypothesis
Null hypothesis (true) Correct decision Type-I error (alpha error)

Null hypothesis (false) Type-II error (beta error) Correct decision

• The probability of Type-I error is usually determined in advance and is understood as the
level of significance of testing the hypothesis.
• If Type-I error is fixed at 5%, it means that there are about 5 chance in
• 100 that we will reject Null hypothesis when Null hypothesis is true. We can control Type-I
error just by fixing at a lower level.
• For instance, if we fix it at 1%, we will say that the maximum probability of committing
Type-I error would only be 0.01.
• But with the fixed sample size, when we try to reduce Type-I error, the probability of
committing Type-II error increases.
• Both types of errors cannot be reduced simultaneously.
• There is trade off between two types of errors which means that the probability of making
one type error can only be reduced if we are willing to increase the probability of making
the other type of error.
• One must set a very high level for Type-I error in one’s testing technique of a given
hypothesis.
• Hence, in the testing of hypothesis, one must make all possible efforts to strike an
adequate balance between Type-I and Type-II errors.
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