1. Types of Studies Observational Study –Observes and measures characteristics without trying to modify the subjects being studied Experiment –Impose a treatment on the subjects, then observe the response

2. Types of Studies Cross-sectional Study –Data are observed, measured, and collected at one point in time. –Example: What percentage of people own dogs? –Most polls are cross-sectional studies

3. Types of Studies Retrospective (or case control) Study –Data are collected from the past –Example: What was the average rainfall in 1994? Prospective (or Longitudinal or Cohort) Study –Data are collected in the future from groups (cohorts) sharing similar characteristics –Example: What percentage of dogs who attend an obedience class are still well-behaved 2 years later?

4. Confounding When it’s not possible to distinguish the effects of each factor (i.e., which factor caused the outcome?) Usually, when there are multiple differences between comparison groups Confounding can be avoided by good study design

5. Examples of Confounding Example: A middle-school implements a new math curriculum. They also encourage parent participation, and offer after-school tutoring. An improvement in performance results Example: An experiment is done to determine if students perform better on tests while listening to music. Each subject is given two similar tests; the first in silence, and the second while listening to music. Performance is higher on the second test.

6. Ways to control confounding Blocks –Create groups with similar characteristics –Ideally identical in every way except factor being compared Blinding –Subjects don’t know if they’re receiving a treatment or placebo Double-blinding –Experimenters don’t know which subjects are receiving the treatment

7. Experiment Design (How to create blocks) Completely randomized experimental design –Subjects are assigned to groups based on a process of random selection Rigorously controlled experimental design –Subjects are very carefully chosen and assigned to groups so they have similar characteristics

8. Sample size Sample Size –Sample must be large enough to reveal the true nature of any effects –Large samples do not make up for bad samples; sample must be selected appropriately for results to be valid.

9. Random Sampling Random Sample –Members of the population are chosen so that each individual has equal likelihood of being chosen Simple Random Sample –A special random sample where every possible sample is equally likely

10. Other types of sampling Systematic sampling –Population is ordered, and every kth element is chosen. –This is only random sampling if the starting element is randomly chosen

11. Other types of sampling Stratified sampling –Population is divided into groups with similar characteristics, and a sample is chosen from each subgroup (stratum) –This is only random sampling if the sample from each subgroup is chosen randomly

12. Other types of sampling Cluster sampling –Divide the population into sections, or clusters. Select a group of clusters, and use all members of those clusters. –This is only random sampling if the clusters to be used are selected randomly

13. Not-so-good sampling methods Voluntary response sample Convenience sample –Choosing whoever’s handy

14. Sources of Error Sampling error –The difference between the sample result and the population result, caused by chance fluctuations Non-sampling error –Error caused by problems in collecting, recording, and analyzing the data (like broken tools, typos, or miscalculations), or by a biased sample (bad sample selection)

15. Differences in error Sampling error is natural and unavoidable. We must consider it when analyzing our data, but we cannot eliminate it. Non-sampling error is avoidable, and every effort should be taken to do so.

16. Homework 1-4: 1-21 odd