1 used for hypothesis testing
and is critical in the
interpretation of results from
quantitative research studies.
1.1 used to see if an association between two (or more)
variables is statistically significant.
1.1.1 determines the probability of obtaining the same or
more extreme result that the one actually observed
from chance alone (i.e. assuming the null
hypothesis is true);
1.1.1.1 generally (but arbitrarily) considered significant if p ≤ 0.05 (i.e.
the p-value is less than or equal, to 0.05).
2 routinely report the results of studies with p-values.
3 If they do not have some test of significance
3.1 you cannot really interpret
whether or not the results are
actually “true” or of any use at
all.
4 results are commonly summarized by a statistical test
4.1 and associated p-values or confidence intervals, and a
decision about the significance of the result is based on
either one of them (they provide similar information)
5 reader decides if evidence is strong enough to believe.
6 study was designed according to good
scientific practice, the strength of the
evidence is contained in the p-value.
7 important for the reader to know what the p-value is saying.
8 If not due to chance, the association is likely to be real
and the results can be generalised to the whole
population.
8.1 the conclusion drawn about the hypothesis
9 The process of applying a test of
significance is also called
HYPOTHESIS TESTING
9.1 this results in a decision to reject, or not, the NULL
HYPOTHESIS.