2. chance Learning impeded by two processes: Bias , Chance
Bias is systematic error.
Bias is unavoidable.
Controlling bias.
Random error resulting from the chance.
It can be minimized but never avoided altogether

3. …. This source of error is called “random”…
Underestimating the importance of chance relative to bias
Observation of samples, whether they are simple description or comparisons, cannot be expected to represent the true situation exactly because of random variation.
Statistical tests

4. TWO APPROACHES TO CHANCE
Hypothesis testing
estimation
- Bayesian reasoning

6. 2 ways of being wrong :
- type 1 error
- type 2 error

7. Because random variation plays a part in all observations, it is an oversimplification to ask whether chance is responsible for the results.
The probability of error due to random variation is to is estimated by means of inferential statistics.
Statistical testing is a means by which the effects of random variation are estimated

8. Expressed by the familiar P value .
P value : P  P

11. Example : Donepezil for the treatment of Alzheime’s disease.

12. Statistical tests are used to estimate the probability of a Type 1 error.
Null hypothesis
Statistical testing is not able to establish that there is no difference at all.

15. Statistical power

17. statistical precision Confidence interval

19. Confidence interval:
- contain the same information as statistical significance.
- have advantages over P values.

21. effect size
characteristics of the data
type 1 error
type 2 error
interrelationships

22. characteristics of the data
interrelationships

24. small sample size that was adequate : NSAIDs and colonic polyps
Example :
small sample size that was adequate : NSAIDs and colonic polyps
large sample size that was inadequate:
low-dose aspirin
and cardiovascular
mortality

25. Sample size based on confidence intervals

27. Chance.
Example
amoxicillin for 3 or 5 days

36. Pearson’s correlations
Spearman’s correlations

37. Multivariable modeling

38. Mathematical models are used in two general ways in clinical researches
To study the independent effect of one variable on outcome while taking into account the effects of other variables that might confound or modify this relationship
To predict a clinical event by calculating the combined effect of several variables acting together
11/14/2018

39. 3. exclude the related variables
4. enter the variables in the model

40. Bayesian reasoning
However Bayesian inference is the conceptual basis for qualitative thinking about cause , as well as for quantitative summaries of clinical trials.
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