Casual and Statistical Reasoning Chapter 8 Pgs 332 334 351 353 355 361 Cause and Effect Reasoning Common in medicine and health care Also see cause and effect reasoning about social problems crime drug use sexual See it in natural phenomena weather environmental May be hard to think of such arguments as inductive rather than deductive because causal relationships are often presented as if they were certain There are uncertainties in causal reasoning however There are inherent limitations on knowing all the possible causal factors of any given behavior event The interaction of causal factors may affect the outcome Arguments Based on Statistical Studies Are inductive arguments since they draw an inference based on a sample group where the evidence is partial at best Always entails a degree of probability in the relationship between the premises and the conclusions We need to know how to properly use statistical studies to recognize strong arguments and avoid being fooled by weak ones Statistical Study has two parts 1 Targeted population about which we want information 2 Sample group we intend to study as a microcosm a community place or situation regarded as encapsulating in miniature the characteristic qualities or features of something much larger of the larger group In certain types of statistical studies medical experiments psychological resting or pharmaceutical studies the research protocol may call for a control group This group is used to compare relative responses in order to eliminate other factors Members of the control our are usually given some sort of placebo to present the subjects from knowing which group they are in One we get evidence we can generalize to the larger targeted population allowing for a certain margin of error Margin of Error recognizes that inference from the smaller sample group to the targeted group is somewhat uncertain Sample group may or may not be representative of the whole Instead of the conclusion that X of A is B a margin of error is added X Z of A is B where Z is the uncertainty in generalizing