Chapter 12 Review Questions (Key) 

§12.1 Introduction

1. Systematic error and random error. 
2. Parameters are error-free quantifications.  Estimates are error-prone statistics.
3. Estimators have hats. Parameters are "hatless.
4. Bias
5. Imprecision.
6. Valid
7. Imprecise
8. Random error is balanced (equal number of overestimates and underestimates), is sample size dependent (less random error in large samples), and can be dealt with via laws of probability.

§12.2 Random Error 

1. False. Probability models only address random error.
2. False. Both subjective and objective probabilities are based on an underlying concept of expected frequencies. In addition, they obey the same mathematical laws.
3. Confidence intervals and hypothesis tests 

§12.3 Systematic Error

1. Selection, information, confounding
2. False. Nondifferential misclassification biases measures of effect toward the null or not at all.
3. False. Bias away from the  null overstates risk. 
4. Confounding is a distortion in a measure of effect brought about by extraneous ("lurking") factors.
5. (a) Associated with the exposure (b) Independent risk factor for disease (c) not intermediate in the causal pathway
6. Confundere = to mix-up
7. Berkson's bias, also know as "Hospital admission rate"
8. No. It will tend to have the same amount of bias. (It will have less random error, however.)
9. Recall bias
10. Information bias
11. When it is not associated with the exposure
12.  
13. It could be considered confounding if the medical indication or the severity of the condition can be measured and adjusted for in the analysis, or it might be considered uncontrolled confounding or selection bias because it acts like a confounder but is a consequence of being "selected" to get the drug.
14.  
15.