1. Chapter 1 Getting Started
What is Statistics?

2. Individuals vs. Variables
People or objects included in the study
Characteristic of the individual to be measured or observed

3. Quantitative vs. Qualitative
Quantitative Variables
Qualitative Variables
Have value or numerical measurement for which operations such as addition or averaging make sense
Describes an individual by placing the individual into a category or group, such as male or female

4. Population vs. Sample Population Data Sample Data
Data is from every individual of interest
Population Parameters are numerical measures that describe an aspect of a population
The data are from only some of the individuals of interest
Sample Statistics are numerical measures that describe an aspect of a sample

5. Levels of Measurement Nominal – Names, Labels, Categories
Ordinal – Arranged in meaningful mathematical order
Interval – Differences are meaningful
Ratio – Division or percentage comparisons make sense; zero point

7. Chapter 1 Getting Started
1.2 Random Samples

8. Simple Random Sample (SRS)
A simple random sample of n measurements from a population is a subset of the population selected in such a manner that every sample of size n from the population has an equal chance of being selected.

9. Random Number Tables (RNT)
Used to help secure a SRS
Steps:
Number all members of the population sequentially.
Drop a pin on the RNT to pick a starting point
Pull digits n at a time, discarding non-used numbers
Repetition?

10. Do Now
With a partner, discuss how a Random Number Table or Random Number Generator could be used to generate the answer key for a multiple choice test (assume 10 questions on quiz and 5 choices per question).
Rephrased: How can a RNT or RNG be used to determine next to which letter the correct answer to each question should be placed?

11. Other Methods to Secure a Sample
Systematic
Stratified
Cluster
Multistage
Convenience

12. Systematic Sampling Population is numbered
Select a starting point at random and pick every kth member

13. Convenience Sampling
Create sample by selecting population members which are easily available

14. Stratified Sampling
Divide population into distinct subgroups based on specific characteristics
Draw random samples from each strata

15. Cluster Sampling
Divide population into pre-existing segments or clusters (often geographic).
Make a random selection of clusters.
All members of cluster are chosen.

16. Multistage Sampling
Use a variety of sampling methods to create successively smaller groups at each stage.
Final sample is made of clusters.

17. Do Now
Copy the Blue Box from page 21 into your notebooks. This is the beginning of Section 1.3 “Introduction to Experimental Design”

18. Census vs. Sample
Census – measurements from observations from the entire population are used.
Sample – measurements from observations from part of the population are used

19. Observational Study vs. Experiment
Observational Study – observations and measurements of individuals are conducted in a way that doesn’t change the response or the variable being measured
Experiment – a treatment is deliberately imposed on the individuals in order to observe a possible change in the response or variable being measured

20. Within Experiments:
Placebo Effect – occurs when a subject receives no treatment but (incorrectly) believes he or she is in fact receiving treatment and responds favorably
Control Group – those who receive the placebo treatment
Treatment Group – those who receive the actual treatment
Completely Randomized Experiment – one in which a random process is used to assign each individual to one of the treatments

21. Completely Randomized Experiment
C.R.E. – is one in which a random process is used to assign each individual to one of the treatments

22. Characteristics of a Well-Designed Experiment
Block – a group of individuals sharing some common features that might affect the treatment
Randomized Block Experiment – individuals are first sorted into blocks, and then a random process is used to assign each individual in the block to one of the treatments

23. Characteristics of a Well-Designed Experiment
Control Groups – used to account for the influence of other known or unknown variables that might be an underlying cause of change in response in the experimental group.
Lurking or Confounding Variables – such variables

24. Characteristics of a Well-Designed Experiment
Randomization – used to assign individuals to the two treatment groups. Helps to prevent bias in selecting members to the groups
Replication – on many patients reduces the possibility that the differences in occurred by chance alone.

25. Potential Pitfalls of Surveys
Nonresponse
Truthfulness of Response
Faulty Recall
Hidden Bias
Vague Wording
Interview Influence
Voluntary Response

26. Data Collection Techniques (Summary)
Census
Samples
Experiments
Observational Studies
Surveys
Simulations (previously)