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180214P - HOW TO TURN RESEARCH QUESTIONS INTO VARIABLES

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Presentation by Prof OHK MBChB (MUK), MPH (Harvard), DrPH (Harvard) Professor of Epidemiology and Bioethics


1.0 RESEARCH QUESTIONS

  • Substantive question
  • Statistical question
  • Statistical answer
  • Substantial answer


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 the 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. H0 and HA are complimentary and exhaustive. 


4.0 VARIABLES USED TO TEST HYPOTHESES (looking for causal relation)

  • Independent/determinant 
  • Dependent/explanatory 
  • Confounding variable


5.0 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.


6.0 DEFINITION OF VARIABLES

  • Variable name or label
  • Variable description 


7.0 CONCLUSIONS and INTERPRETATIONS

  • A statistically significant test implies that the following are true: H0 is false, H0 is rejected, 
  • A statistically nonsignificant 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 is 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 are not discriminating enough.