Statistics
the study of how to collect, organize, analyze, and interpret numerical info from data
Individuals
people or objects being studied
Variable
characteristic of the individual to be measured or observed
Quantitative
has a value or numerical measure for which operations such as addition or averaging make sense
Qualitative
describes an individual by placing the individual into a category or group (eg male or female)
Population Data
data are from EVERY individual of interest
Sample Data
data are from only SOME of the individuals
Parameter
numerical measure that describes an aspect of a population
Statistic
numerical measure that describes an aspect of a sample
Levels of Measurement
levels that indicate the type of arithmetic that is appropriate for the data (ordering, taking differences, taking ratios)
Nominal Level
applies to data that consists of names, labels, or categories
Ordinal Level
applies to data that can be arranged in order. However, differences between data values either cannot be determined or are meaningless
Interval
applies to data that can be arranged in order. In addition, differences between data values are meaningful
Ratio
applies to data that can be arranged in order. In addition, both differences between data values and ratios of DV are meaningful. Data at the ratio level have a true zero
Descriptive Statistics
involves methods of organizing, picturing, and summarizing info from samples or population
Inferential Statistics
involves methods of using info from a sample to draw conclusions regarding the population
Simple Random Sample
a subset of a population selected in a manner such that every section sample of size n from the population has an equal chance of being selected
Simulation
numerical facsimile or representation of a real-world phenomenon
Random Number Table
a table of numbers that makes it easier to select numbers in a random sample
Sampling with Replacement
although a number is selected for the sample, it is not removed from the population (the same number may be selected for the sample more than once)
Stratified Sampling
divide the entire population into distinct subgroups called strata. The strata are based on shared characteristics such as age, income, and education level. Draw random samples from each stratum
Systematic Sampling
number all members of the population sequentially. Then, from a starting point selected at random, include every kth member of the population in the sample
Cluster Sampling
divide the entire population into pre-existing segments or clusters. The clusters are often geographic. Make a random selection of clusters. Include every member of each selected cluster in the sample
Multistage Samples
use a variety of sampling methods to create successively smaller groups at each stage. The final sample consists of clusters
Sampling Frame
a list of individuals from which a sample is actually selected
Undercoverage
results from omitting population members from the sample frame
Sampling Error
the difference between measurements from a sample and corresponding measurements from the respective population. It is caused by the fact that the sample does not perfectly represent the population
Nonsampling Error
the result of poor sample design, sloppy data collection, faulty measuring instruments, bias in questionnaires, and so on
Census
measurements or observations from the entire population are used
Sample
measurements or observations from part of the population are used
Observational Study
observations and measurements of individuals are conducted in a way that doesn’t change the response or the variable being measured
Experiment
treatment is deliberately imposed on the individuals in order to observe a possible change in the response or variable being measured
Placebo Effect
occurs when a subject receives no treatment but (incorrectly) believes he or she is in fact receiving treatment and responds favorably
Completely Randomized Experiment
one in which a random process is used to assign each individual to one of the treatments
Convenience Sample
create a sample by using data from population members that are readily available
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
Control Group
group not given treatment, but everything else is the same so that any difference in variable(s) can be attributed to the treatment
Randomization
used to assign individuals to the two treatment groups. This helps prevent bias in selecting members for each group
Replication
repeating of an experiment on many patients to reduce the possibility that the observed changes occured by chance alone
Treatment Group
group that is given the treatment
Confounding Variable
two variables whose individual effects cannot be distinguished
Lurking Variable
one for which no data have been collected but that nevertheless has influence on the other variables in the study
Double-blind Experiment
neither the subject nor the observer knows which subjects are receiving treatment
Survey
a process of gathering data by asking questions
Nonresponse
individuals either cannot be contacted or refuse to participate. Can result in significant undercoverage of a population
Voluntary Response
individuals with strong feelings about a subject are more likely than others to respond. Such a study is interesting but not reflective of the population
Hidden Bias
the question may be worded in such a way as to elicit a specific response. The order of questions might lead to biased responses
Generalizing Results
results that are valid for a particular group may or may not be valid for a different group
Study Sponsor
the sponsor of a study may advertently or inadvertently affect the outcomes or finding