Exam 1 Stat

Chapter 3
- Statistics vs parameters
- Proportion definition and notation
- p -> population proportion, 𝑝̂ -> sample proportion
- 𝑝̂ = x/n", where x is number of interest and n is sample size
- N vs n
- What occurs when N = n?
- Behavior of sample statistics
- Because of sampling variability, sample statistics also vary

- The larger the sample, the less the sample statistic varies
- An unbiased sample statistic is centered around the true value
- Types of error in estimation – bias and variability
- How to eliminate bias? How to reduce variability?
- Properties of good sampling methods
- Concept of margin of error (formula given on exam)
- When is MOE reliable?
- How to use MOE to get an interval estimate rather than a point estimate
- Point estimate = best guess for the true value, single sample statistic
- Confidence statements
Chapter 4
- Calculating response and nonresponse rate
- Types of errors in sample surveys
- Sampling errors – biased sampling design, random error
- Nonsampling errors – nonresponse, misleading questions, false responses
- Response error – accidental vs purposeful
- When and why to use the randomized-response technique
- Stratified vs clustered random sampling designs
- Systematic random sampling
Chapter 5
- Statistical significance
- Issues and limitations to consider in studies
- Randomized comparative experiment and procedures
- Confounding effects
- Confounding vs lurking variable
- Treatment vs control
- One-track experiment
- Placebo and Hawthorne effect
- Blinding in studies

Chapter 6
- Principles of experimental design
- Clinical trials and efficacy
- Differences in clinical trial phases
- Nonadherence, dropouts, lack of realism
- Concept of equipoise
- Role of Data Safety Monitoring Boards
Chapter 7
- Role of IRBs
- Qualifications IRBs need for approval
- Informed consent
- Confidentiality vs anonymity
- Tuskegee syphilis experiment