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Presented at a workshop on evidence-based decision making organized by the Ministry of Health Kingdom of Saudi Arabia Riyadh 24-26 April 2010 by Professor Omar Hasan Kasule MB ChB (MUK), MPH (Harvard), DrPH (Harvard) Professor of Epidemiology and Bioethics Faculty of Medicine King Fahd Medical College
1.0 RESEARCH QUESTIONS
An investigator starts with a substantive question that is formulated as a statistical question. Data is then collected and is analyzed to reach a statistical conclusion. The statistical conclusion is used with other knowledge to reach a substantive conclusion.
The substantive question may be posed as a simple yes/no like is this policy better than that policy? However it may be posed in a comparative way requiring a unit of measurement and comparison for example one policy may be twice as effective as another one.
2.0 HYPOTHESES AND THE SCIENTIFIC METHOD
The scientific method consists of hypothesis formulation, experimentation to test the hypothesis, and drawing conclusions. Hypotheses are statements of prior belief. They are modified by results of experiments to give rise to new hypotheses. The new hypotheses then in turn become the basis for new experiments.
3.0 NULL HYPOTHESIS (H0) & ALTERNATIVE HYPOTHESIS (HA):
The null or research hypothesis, H0, states that there is no difference between two comparison groups and that the apparent difference seen is due to sampling error. The alternative hypothesis, HA, disagrees with the null hypothesis.
4.0 HYPOTHESIS TESTING USING P-VALUES
The main parameters of hypothesis testing are the significance level and the p-value. The pre-set level of significance customarily set at 0.05, is the probability of wrongfully rejecting H0 5% of the time, a ratio of 1:20. The p value is calculated from the data using complicated formulas. The p value can be defined in a commonsense way as the probability of rejecting a true hypothesis by mistake. The decision rules are: If the p < 0.05 H0 is rejected (test statistically significant). If the p>0.05 H0 is not rejected (test not statistically significant).
5.0 CONCLUSIONS and INTERPRETATIONS
A statistically significant test implies that the following are true: H0 is false, H0 is rejected, observations are not compatible with H0, observations are not due to sampling variation, and observations are real/true biological phenomena. A statistically non significant test implies the following are true: H0 is not false (we do not say true), H0 is not rejected, observations are compatible with H0, observations are due to sampling variation or random errors of measurement, and observations are artificial, apparent and not real biological phenomena. Statistical significance may have no clinical/practical significance/importance. This is due to other factors being involved but are not studied. It may also be due to invalid measurements. Practically important differences may not reach statistical significance due to small sample size or due to measurement that are not discriminating enough.