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Lecture at a program “Research Methodology in Health Sciences Course” held at Northern Area Armed Forces Hospital, Hafr Al Batin on 28 December 2020 at 3.00pm. By Professor Omar Hasan Kasule MB ChB (MUK), MPH (Harvard), DrPH (Harvard) Chairman, Institutional Review Board - KFMC
RESEARCH / STUDY QUESTIONS:
}
Substantive question.
}
Statistical question.
}
Statistical answer.
}
Substantial answer.
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 the results of experiments to give rise to new hypotheses.
} The new hypotheses than in
turn become the basis for new experiments.
NULL HYPOTHESIS (H0) & ALTERNATIVE HYPOTHESIS (HA):
} The null or research
hypothesis, H0, states that there is no difference between the two comparison groups and that the apparent
difference seen is due to sampling error.
} The alternative hypothesis,
HA, disagrees with the null hypothesis. H0 and HA
are complimentary and exhaustive.
VARIABLES USED TO TEST HYPOTHESES (looking for causal relation)
}
Independent/determinant.
}
Dependent/explanatory.
}
Confounding variable.
TYPES OF VARIABLES:
} Qualitative variables are
attribute or categorical with no intrinsic numerical value.
} Quantitative (numerical)
discrete random variables are based on counting and have no decimals. They are
categorical 2 (dichotomy) >3 (polychotomy)
} Quantitative (numerical)
continuous random variables are based on the measurement and have decimals.
DEFINITION OF VARIABLES:
}
Variable name or label.
}
Variable description.
CONCLUSIONS AND INTERPRETATIONS:
} A statistically significant test implies that
the following are true: H0 is false, H0 is rejected.
} A statistically non-significant test implies the following are true: H0 is not false (we do not say true), H0 is not rejected.
} Statistical significance may have no clinical/practical significance/importance. This is due to other factors being involved but not studied. It may also be due to invalid measurements. Clinically important differences may not reach statistical significance due to a small sample size or due to measurements that is not discriminating enough.