EBM Exam 1

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Rx2013
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132000
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EBM Exam 1
Updated:
2012-01-31 12:04:58
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Biostats1
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Biostats 1
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  1. Sample
    • must be representative of the entire population
    • generizability
  2. Random sampling
    • evenly distributed patient characteristics
    • equal chance of being in the study
  3. Paired sample
    each subject has a matching mate in the other group
  4. independent sample
    • subjects are not matched as pairs
    • larger number of patients needed
  5. Data types
    • interval
    • ratio
    • ordinal
    • nominal
  6. Interval data
    • continuous
    • known, equal distance between each interval
    • may contain negative numbers
    • ex. temperature
  7. Ratio data
    • continuous
    • known, equal distance between each interval
    • non-arbitrary zero
    • no negative numbers
    • ex. age, wt, BG, BP, incidence rates of outcomes
  8. Ordinal Data
    • categorical
    • ranked
    • intervals not equal
    • ex. pain scale, military rank
  9. Nominal Data
    • categorical
    • cannot be ranked
    • binomial or non-binomial
    • binomial ex. mortality, gender
    • non-binomial exp. eye color, hair color, ethnicity
  10. statistical methods/test are described as being
    parametric or non-parametric
  11. When do you use parametric tests?
    • data from sample results numerically describe a characteristic
    • assumes normal distribution
    • applies to most interval and ratio data
  12. which are more powerful, parametric tests or non-parametric tests?
    parametric tests
  13. when do you use non-parametric tests?
    • no assumption reguarding distribution
    • applies to ordinal and nominal data
    • can be applied to interval and ratio data when distribution is skewed
  14. specific characteristic determining use of parametric/non-parametric tests
    • type of data
    • assumed distribution of data
    • how many groups a study includes
  15. parametric test examples
    • student's t-test - 2 groups
    • anova, ancova - >2 groups
  16. non-parametric test examples
    • chi square test
    • fisher's exact test - smaller #
    • mann-whitney U test
    • Wilcoxin test
    • Kruskal-wallis test
  17. Null hypothesis
    • theory about an outcome that a study is designed to test
    • null = no difference between study groups
  18. Alternate hypothesis
    • opposite of null hypothesis
    • there is a difference between study groups
  19. alpha
    level of significance
  20. we usually classify something as statistically significant if it
    falls below a certain level of significance
  21. statistical & clinical significance
    • may be statistically significant but not clinically important
    • if it is not statistically significant it cannot be clinically important
  22. p-value
    • chosen level of significance
    • usually p<0.05
    • describes the probability that differences between 2 groups occured by chance
  23. if p-value < alpha
    • statistically significant
    • reject null hypothesis
  24. if p-value > alpha
    • not statistically significant
    • fail to reject null hypothesis
  25. setting alpha establishes
    probability of making a type I error
  26. type I error
    • incorrectly reject null hypothesis
    • false positive
    • say there is a difference when there isn't
  27. setting Beta establishes
    • probablility of making a type II error
    • conventionally set at 0.2
  28. Type II error
    • incorrectly fail to reject the null hypothesis
    • false negative
    • say there is no difference when there may be one
    • associates with too small sample population
    • used in calculating power

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