P-values

used for hypothesis testing and is critical in the interpretation of results from quantitative research studies.
1. used to see if an association between two (or more) variables is statistically significant.
  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. generally (but arbitrarily) considered significant if p ≤ 0.05 (i.e. the p-value is less than or equal, to 0.05).
routinely report the results of studies with p-values.
If they do not have some test of significance
1. you cannot really interpret whether or not the results are actually “true” or of any use at all.
results are commonly summarized by a statistical test
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)
reader decides if evidence is strong enough to believe.
study was designed according to good scientific practice, the strength of the evidence is contained in the p-value.
important for the reader to know what the p-value is saying.
If not due to chance, the association is likely to be real and the results can be generalised to the whole population.
1. the conclusion drawn about the hypothesis
The process of applying a test of significance is also called HYPOTHESIS TESTING
1. this results in a decision to reject, or not, the NULL HYPOTHESIS.
GOLDEN RULES
1. p ≤ 0.05
  1. the result is statistically significant
2. p > 0.05
  1. the result is NOT statistically different

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P-values

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