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RSS Hypothessis testing

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Hypothessis testing by Dr. O. Yusuf as part of the 5th Research Summer School - Jeddah at KAIMRC - WR

Hypothessis testing by Dr. O. Yusuf as part of the 5th Research Summer School - Jeddah at KAIMRC - WR


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  • 1. SamplingDistributions, StandardError, Confidence IntervalOyindamola Bidemi YUSUFKAIMRC-WR
  • 2. SAMPLE Why do we sample? Note: information in sample may notfully reflect what is true in thepopulation We have introduced sampling error bystudying only some of the population Can we quantify this error?
  • 3. SAMPLING VARIATIONS Taking repeated samples Unlikely that the estimates would be exactlythe same in each sample However, they should be close to the truevalue By quantifying the variability of theseestimates, precision of estimate is obtained. Sampling error is thereby assessed.
  • 4. SAMPLING DISTRIBUTIONS Distribution of sample estimates- Means- Proportions- Variance Take repeated samples and calculateestimates Distribution is approximately normal
  • 5.  Mathematicians have examined thedistribution of these sample estimatesand their results are expressed in thecentral limit theorem
  • 6. central limit theorem Sampling distributions are approximately normallydistributed regardless of the nature of the variable inthe parent population The mean of the sampling distribution is equal to thetrue population mean Mean of sample means is an unbiased estimate ofthe true population mean The standard deviation (SD) of sampling distributionis directly proportional to the population SD andinversely proportional to the square root of thesample size
  • 7. SUMMARY: DISTRIBUTION OFSAMPLE ESTIMATES NORMAL Mean = True population mean Standard deviation = Population standarddeviation divided by square root of samplesize Standard deviation called standard error
  • 8. ESTIMATION A major purpose or objective of healthresearch is to estimate certain populationcharacteristics or phenomena Characteristic or phenomenon can bequantitative such as average SYSTOLICBLOOD PRESSURE of adult men or qualitativesuch as proportion with MALNUTRITION Can be POINT or INTERVAL ESTIMATE
  • 9. Point estimates Value of a parameter in a populatione.g. mean or a proportion We estimate value of a parameter usingusing data collected from a sample This estimate is called sample statisticand is a POINT ESTIMATE of theparameter i.e. it takes a single value
  • 10. STANDARD ERROR Used to describe the variability ofsample means Depends on variability of individualobservations and the sample size Relationship described as –Standard error = Standard DeviationSquare root of samplesize
  • 11. Sample 1 MeanSample 2 MeanSample 3 Mean……….….........Sample n MeanStandard errorMean of the meansMean of the meansThis mean will also have a standard deviation= SEStandard error
  • 12. Standard Deviation or StandardError? Quote standard deviation if interest is in thevariability of individuals as regards the levelof the factor being investigated – SBP, Ageand cholesterol level. Quote standard Error if emphasis is on theestimate of a population parameter.It is a measure of uncertainty in the samplestatistic as an estimate of populationparameter.
  • 13. Interpreting SE Large SE indicates that estimate isimprecise Small SE indicates that estimate isprecise How can SE be reduced?
  • 14. Answer If sample size is increased If data is less variable
  • 15. INTERVAL ESTIMATE Is SE particularly useful? More helpful to incorporate this measure ofprecision into an interval estimate for thepopulation parameter How? By using the knowledge of the theoreticalprobability distribution of the sample statistic tocalculate a CI
  • 16.  Not sufficient to rely on a singleestimate Other samples could yield plausibleestimates Comfortable to find a range of valueswithin which to find all possible meanvalues
  • 17. WHAT IS A CONFIDENCEINTERVAL? The CI is a range of values, above and below afinding, in which the actual value is likely to fall. The confidence interval represents the accuracy orprecision of an estimate. Only by convention that the 95% confidence levelis commonly chosen. Researchers are confident that if other surveyshad been done, then 95 per cent of the time — or19 times out of 20 — the findings would fall in thisrange.
  • 18. CONFIDENCE INTERVAL Statistic + 1.96 S.E. (Statistic) 95% of the distribution of samplemeans lies within 1.96 SD of thepopulation mean
  • 19. Interpretation If experiment is repeated many times,the interval would contain the truepopulation mean on 95% of occasions i.e. a range of values within which weare 95% certain that the truepopulation mean lies
  • 20. Issues in CI interpretation How wide is it? A wide CI indicates thatestimate is imprecise A narrow one indicates a preciseestimate Width is dependent on size of SE, whichin turn depends on SS
  • 21. Factors affecting CI A narrow or small confidence intervalindicates that if we were to ask the samequestion of a different sample, we arereasonably sure we would get a similar result. A wide confidence interval indicates that weare less sure and perhaps information needsto be collected from a larger number ofpeople to increase our confidence.
  • 22.  Confidence intervals are influenced bythe number of people that are beingsurveyed. Typically, larger surveys will produceestimates with smaller confidenceintervals compared to smaller surveys.
  • 23. Why are CIs important Because confidence intervals representthe range of values scores that arelikely if we were to repeat the survey. Important to consider whengeneralizing results. Consider random sampling andapplication of correct statistical test Like comfort zones that encompass thetrue population parameter
  • 24. Calculating confidence limits The mean diastolic blood pressure from16 subjects is 90.0 mm Hg, and thestandard deviation is 14 mm Hg.Calculate its standard error and 95%confidence limits.
  • 25. Standard error = Standard DeviationSquare root of samplesize14√16
  • 26.  95% CI: Statistic + 1.96 S.E. (Statistic)
  • 27. ANWERS Standard error – 3.5 95% confidence limits – 82.55 to 97.46
  • 28. CI for a proportion P + 1.96 S.E. (P) SE(P)= √p(1-p)/n Online calculators are available
  • 29. In summary SD versus SE Meaning and interpretation of CI Shopping for the right samplingdistribution
  • 30.  THANK YOU