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Research Method Year One
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Psychology Research MethodChapter 2: Introduction to Research Method II
Topic 1: Introduction to hypothesis testing in psychology
Hypothesis testing in psychology
The main purpose of science is to build knowledge of the world and to
understand how things, both inanimate and biological, work. A basic
assumption in this endeavour is that things are caused by other things -
everything has a cause and it is by understanding the causal nature of the
world that we build our knowledge of it.
In designing an experiment, one tries to recreate the conditions under which it
should be possible to explain why one thing (X) causes something else (Y) to
occur.
An example might be that I believe my partner will be happier if I always
remember to laugh at his or her jokes and funny stories. So, I might test this by
laughing loudly at my partner’s jokes and funny stories (X) to see if it causes my
partner to smile more (Y). In an experiment, one or more instances of (X) are
created under carefully controlled conditions to see if they cause (Y) to occur,
so every time your partner tells a joke or a funny story, you laugh out loud.
Recording your results might confirm or disconfirm this prediction about the
causal relationship between your partner being happy and your behaviour
towards them. This isn’t very scientific, however; it’s just an everyday example
of how to test a causal explanation.
Therefore, the scientific experiment is carried out with the main aim of testing
an explanation: There is some observation of human behaviour (either from
other experiments or from some publicly observable event) which requires an
explanation. A theory is developed that attempts to explain the observations.
A good theory should be able to explain not just a single observation, but
numerous observations of certain types of behaviour. The scientific
psychologist then tries to scrutinise the theory empirically - that is, he or she
develops a testable hypothesis that should be true if the theory is true and
then puts it to the test in one or more experiments.
We use inferential tests to assess the hypothesis in an experiment. Inferential
tests offer us the probability of observing the difference in scores between two
groups of participants or two versions of a test if we assume that the results
are only due to chance.
,Psychology Research Method
The use of inferential statistics
Think of a number between one and ten
Suppose two groups of students (call them Group A and Group B, with 100 in
each group) were asked to think of a number between 1 and 10. Suppose I
added up the numbers given by Group A and divided this by 100. I would have
the mean number generated by Group A. I then find the mean number for
Group B.
Now, provided that both groups were asked the same question and in precisely
the same way, and also generated their response in the same way (e.g., by
writing it down on a blank piece of paper), there is no reason to believe that
the mean of Group A should be much different from the mean of Group B. We
would not expect the two means to be exactly the same, but very close.
So, we might have a mean Group A score of 5.51 and a mean Group B score of
5.68. The difference is only 0.17.
Suppose we are told that in fact, one of the following occurred during the task:
(1) The female students in Group A were secretly instructed to produce their
number between 7 and 10.
(2) The female students in Group B were secretly instructed to produce their
number between 7 and 10.
(3) All of the students in both groups were given the same instructions -
produce a number between 1 and 10.
Our task is to work out which one of these three possibilities (or hypotheses)
was true. I do this by comparing the means of Group A and Group B. If the
means are quite close (e.g., 5.51 versus 5.68, as in the example above), I would
most likely conclude that (3) was true - that both groups were given the same
instructions.
However, if the mean of one group was quite a bit higher than the other (e.g.,
5.51 versus 7.85, which is a difference of 2.34), then I would be tempted to
conclude either (1) or (2) depending on which group had the higher mean.
This exercise raises the question - what difference would we except for either
(1) or (2)? How close do the scores need to be to assert (3)?
,Psychology Research Method
This is precisely the problem faced when one looks at the data gathered in an
experiment. Any difference between two means could be due to the
independent variable (e.g., the type of instructions given) or could simply be
due to chance or random variation (caused by something other than our
independent variable). Furthermore, while a difference of 2.5 between the two
means implies that one group was given “special” instructions and the other
was not, what should I conclude if the difference was 1.2 or 0.6, or 0.3? Where
should I draw the line?
The answer is: Use probability to estimate what an acceptable difference is. A
small difference between the mean scores of Group A and Group B is highly
likely to occur if (3) is correct. A very large difference between the mean scores
of Group A and Group B is highly unlikely to occur if (3) is correct.
Example of probability to estimate acceptance of a hypothesis: Finger
tapping
This second example is taken from Howell (2007) on how we might use
probability to determine whether a person is from a population of
neurologically healthy people or not.
Suppose the mean rate of finger tapping in normal healthy adults is 100 taps
per 20 seconds, with a standard deviation of 20, and that tapping speeds are
normally distributed in the population. Suppose we also know that the tapping
rate is slower among people with certain neurological problems.
Suppose further that we have just tested a person who taps at the rate of 70
taps in 20 seconds. Is this score sufficiently lower than the mean for us to
assume the person is not neurologically healthy (see Figure 1.01)?
, Psychology Research Method
Figure 1.01
In Figure 1.01, the heavy black line represents the mean tapping score of the
population of neurologically healthy adults. The thin vertical black line is the
score of a person we have just tested. It is possible to calculate the probability
of obtaining a score of 70 or below from the population of neurologically
healthy adults. Graphically, it is the area under the curve from scores of 70 and
below. In fact, the probability is calculated as .067 and we can say that 6.7%
have a tapping score of 70 and below (we do not need to know how this was
calculated at this stage). We must then decide whether 6.7% is sufficiently
unlikely (and hence whether 70 is sufficiently below 100 in this test). One
convention is that we use the criterion of .05 (or 5%) such that if the computed
probability is less than or equal to .05, then we accept our hypothesis that
there is a difference in scores. In our example, since .067 is slightly larger than
.05, we can say that our hypothesis that the person is not neurologically
healthy cannot be accepted, and so we have no reason to decide that the
person did not come from a population of healthy people. We have not proved
that this person is healthy, but rather we can only conclude that this person
does not tap sufficiently slowly for an illness to be statistically detectable. To
help you understand how to interpret probability values, see Figure 1.02.
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