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

…. 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

TWO APPROACHES TO CHANCE Hypothesis testing estimation - Bayesian reasoning

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

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

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

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

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.

Statistical power

statistical precision Confidence interval

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

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

characteristics of the data interrelationships

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

Sample size based on confidence intervals

Chance. Example amoxicillin for 3 or 5 days

Pearson’s correlations Spearman’s correlations

Multivariable modeling

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

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

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