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Arrangements or patterns for producing data are called designs Anecdotal Evidence • Evidence based on haphazardly selected individual cases, which often come to our attention because they are striking in some way. – One of the scientific rules is that anecdotal evidence doesn't cut it (at least not alone). – The main value of the collection of anecdotal evidence is that it might inspire someone to look into the matter properly. Basic Terms • Available data are data that were produced in the past for some other purpose but that may help answer a present question. • Population: The collection of individuals or items of interest – examples: all residents of KY, all hospitals in U.S., all trees in a forest • Sample: The subset of the population on which we make measurements. We call the measurements data. • Experiment: a planned activity whose results yields data – Select 3 people and record weight – Flip 8 coins and count # of heads • Sampling frame: list of elements in population • Simple random sample: sample selected such that all possible samples have an equal chance of being selected Inferential Statistics • Methods of making inference about a population based on the information in a sample • We wish to control the probability of errors – – – – How do we select the sample? How large is the sample? How do we collect the data? How do we analyze the data? Types of Data • qualitative: non-numerical categorization – Gender, religion preference, hair color • quantitative: numerical data • discrete: things that can be counted – # of children, # of accidents • continuous: things that are measured – time, weight, distance • statistic: a numerical characteristic of a sample • parameter: a numerical characteristic of a population Design of Experiments: Basic Vocabulary • The individuals on which the experiment is done are the experimental units. – If human, they are called subjects. • Treatments are specific experimental conditions applied to the units. • Independent variables in an experiment are often called factors. – Specific values of these factors are classified as levels. Principles of Statistical Design of Experiments • Control the effects of lurking variables on the response, most simply by comparing two or more treatments • Randomize: use chance to assign subjects to the treatments – Table of random digits – Random number generator • Replicate: reduces the role of chance variation and makes the experiment more sensitive to differences among the treatments Sampling Design • Voluntary Response Sample: consists of people who choose themselves by responding to a general appeal • Simple Random Sample: consists of n individuals from the population chosen in such a way that every set of n individuals has an equal chance to be the sample actually selected • Probability Sample: sample chosen by chance; must know what samples are possible, and what probability each possible sample has • Stratified Random Sample: Divides population into groups of similar individuals, called strata, then chooses separate simple random samples in each stratum and combines these to form the full sample • Multistage samples select successively smaller groups within the population in stages, resulting in a sample consisting of clusters of individuals. Each stage may employ a simple random sample, a stratified sample, or another type of sample.