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Why do experimenters do tests of significance? – 1. To be sure that the I.V is responsible for results & 2.
That results aren’t due to chance.
So if the p value is < 0.05, e.g. the calculated p value = 0.04 your conclusion is
the results are statistically significant & there is a 4% probability that the difference between the
results of the 2 conditions was due to chance alone
Alternatively, you could state - that there was a 96% probability that the difference between the
results of the 2 conditions was due to the IV & not due to chance.
No significant difference – indicates that the observed differences are due to chance
Booze bus analogy: If the calculated p value is under 0.05 you are O.K
the hypothesis is supported
But if p value is over 0.05, then the possibility of chance factors
influencing the result is over the limit, thus hypothesis is rejected
A Conclusion – is a final decision about what the results obtained from an investigation mean.
i.e. whether the hypothesis is supported or rejected based on the p value obtained.
E.g. the faster the tempo of the music listened to by the participants driving on an open road, the greater the
speed limit was exceeded. .
You can conclude a treatment has worked if the results for the experimental are significant (p < 0.05).
The conclusion only relates to the sample group tested, so it’s O.K to make a conclusion even if there are
extraneous variables.
Make sure your conclusion contains the IV & DV.
Evaluation of research in terms of generalising the findings to the
– is a judgment about the extent to which the research findings can be applied
to the population (from which the sample was drawn)
 Note: generalising the results to the sample cannot be done; rather generalising the results to the
population can be done
 Generalisations can be risky – if confounding or extraneous variables were present
Examples of reasons why results of an experiment might not be generalised
Non random selection of participants
Participants volunteered, hence were not representative of the population (since not everyone in population
had an equal chance of being selected)
Gender of participants was NOT controlled i.e. one group was mainly females, the other was mainly males,
thus creating a gender bias.
There might be order effects due to use of repeated measures design – e.g. (for IQ tests – people can
improve results on certain tests)
Sample size is too small compared to the actual population
Participant expectancy (placebo) effects might have contributed to the outcome (a single blind procedure
was not used)
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