Statistics Chapter 5 Producing Data: Samples and Experiments

1. Statistics Chapter 5Producing Data:Samples and Experiments

2. Statistics Statistics is the science of gaining information from numerical data 1. Individual-objects described by data (people, plants) • 2. Variable-characteristic measured • Categorical – labels (gender, political party) • Quantitative – numerical value ( height, IQ) 3. Distribution – Tells us about variables and how often variable takes a value

3. Statistic: 4 out of 5 dentists prefer GT toothpaste

4. Displaying Data

6. Group Work • Find an interesting statistic and discuss any problems you feel would have been encountered obtaining the necessary data. • Find an interesting visual display of data and explain what you believe the main point the author was trying to have readers believe.

7. Sampling Terms: Population – entire group we want info about Sample – part of population we actually examined Census – attempt to contact every individual Design – method used to choose sample Biased – a study is biased if the design systematically favors certain outcomes.

8. Sampling Design: Judgmental sample – individual chooses sample Voluntary Response – People who respond choose themselves Convenience Sampling – choose the people easiest to reach Simple Random Sample – each group of “n” individual is equally likely to be chosen

9. Systematic Sample – Choose a random number “n” and ask every nth person Stratified Random Sample – divide population into groups of similar individual “strata”, then choose a random sample from each Multistage Sample – Sample of counties Sample of towns in counties Sample of streets in towns 8. Cluster Sample – Randomly choose one or more “clusters” of data

10. Problems with Sampling Non-response – can’t contact or refuse to participate Undercoverage – group of population is left out in the process of choosing the sample Response Bias – Wording of question Extra information Characteristics of the interviewer anonymity

11. Association vs Causation Causation- Change in x causes the change in y Causation- Opposite of what is presented. Change in y causes the change in x Y Y X X Common Response – x and y both respond to change in unobserved variable X Y Z

12. X Y Confounding – effect of x on y is hopelessly mixed up with other variables effect on y Z Note: Causation can only be determined with a well-designed experiment!

13. Experimental Design Terms Terms: Response variable – measures outcome (dependent, y) Explanatory variable – attempts to explain response (independent, x) Experimental Units – individuals being studied Treatment – specific experimental condition Level – amount or degree of treatment

14. Placebo - dummy treatment response to a placebo is call the placebo effect Control group (given placebo, no treatment, or accepted treatment – helps control lurking variables) Statistically significant – observed effect too large to attribute to chance

15. Principles of Experimental Design 1) Control – to counter effects of lurking variable(s) (simplest form is comparison) 2) Randomization – use impersonal chance to assign subjects to treatment. It is used to make the treatment groups as equal as possible and to spread the lurking variables throughout all groups 3) Replication – Repeat the experiment on many subjects to reduce the chance variation in the results.

16. Types of experiments Blind – recipient does not know treatment Double Blind – recipient and tester do not know treatment Block Design – A block is a group of experimental units that are similar in ways that are expected to affect the response of the treatment (randomly assign within block) Justify reason for blocks! Blocking reduces variability within sample and offer control over lurking variables such as gender, age, etc Matched pairs – Blocks are “alike” to better see result of treatment (twin studies) Can also compare before and after of same individual.