Chapter 1 Getting Started What is Statistics?

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

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

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

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

Chapter 1 Getting Started 1.2 Random Samples

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.

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?

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?

Other Methods to Secure a Sample Systematic Stratified Cluster Multistage Convenience

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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