Gathering Data Flashcards
Random
An outcome is random if we know the possible values it can have, but not which particular value it takes
Simulation
Models a real world simulation by using random-digit outcomes to mimic the uncertainty of a response variable of interest
Simulation component
A component uses equally likely random digits to model simple random occurrences whose outcomes may not be equally likely
Trial
The sequence of several components representing events that we are pretending will take place
Response variable
Values of the response variable record the results of each trial with respect to what we were interested in
Population
Entire group of individuals or instances about whom we hope to learn
Sample
A representative subset of a population, examined in hope of learning about the population
Sample survey
A study that asks questions of a sample drawn from some population in the hope of learning something about the entire population.
Ex. Polls taken to assess voter preferences are common sample surveys
Bias
Any systematic failure of a sampling method to represent its population is bias. Tends to over or underestimate parameters. It is almost impossible to recover from bias, so efforts to avoid it are well spent. Common errors include
- relying on voluntary response
- undercoverage of the population
- Nonresponse bias
- Response bias
Randomization
The best defense against bias is randomization, in which each individual is given a fair, random chance at selection.
Sample size
The number of individuals in a sample. The sample size determines how well the sample represents the population, not the fraction of the population sampled.
Census
A sample that consists of the entire population
Population parameter
A numerically valued attribute of a model for a population. We rarely expect to know the true value of a population parameter, but we do hope to estimate it from sampled data.
Ex. The mean income of all employed people in the country is a population parameter.
Representative
A sample is said to be representative if the statistics computed from it accurately reflect the corresponding population parameters.
Simple random sample
A simple random sample of size n is a sample in which each set of n elements in the population has an equal chance of selection
Sampling frame
A list of individuals from whom the sample is drawn is called the sampling frame. Individuals who may be in the population of interest, but who are not in the sampling frame, cannot be included in any sample.
Sampling variability
The natural tendency of randomly drawn samples to differ from each other. Sometimes called sampling error, sampling variability is not error, but just the natural result of random sampling.
Stratified random sample
A sampling design in which the population is divided into several sub populations, or strata, and random sample are then drawn from each stratum. If the strata are homogenous, but are different from each other, a stratified random sample may yield more consistent results than an SRS.
Cluster sample
A sampling design in which entire groups, or clusters, are chosen at random. Cluster sampling is usually selected as a matter of convenience, practicality, or cost. Clusters are heterogeneous, and a random sample of clusters should be representative of the population.
Multistage sample
Sampling schemes that combine several sampling methods are called multistage samples. For example, a national polling service may stratify the country by geographical regions, select a random sample of cities from each region, and then interview a cluster of residents in each city.
Systematic sample
A sample drawn by selecting individuals systematically from a sampling frame. When there is no relationship between the order of the sampling frame and the variables of interest, a systematic sample can be representative.
Pilot study
A small trial run of a survey to check whether questions are clear. A pilot study can reduce errors due to ambiguous questions.
Voluntary response bias
Bias introduced to a sample when individuals can choose on their own whether to participate in the sample. Samples based on voluntary response are always invalid and cannot be recovered, no matter how large the sample size.
Convenience sample
A convenience sample consists of the individuals who are conveniently available. Convenience samples often fail to be representative because every individual in the population is not equally convenient to sample.
Undercoverage
A sampling scheme that biases the sample in a way that gives a part of the population less representation than it has in the population suffers from undercoverage.
Nonresponse bias
Bias introduced when a large fraction of those sampled fails to respond. Those who do respond are likely to not represent the entire population. Voluntary response bias is a form of nonresponse bias, but nonresponse may occur for other reasons. For example, those who are at work during the day won’t respond to a telephone survey conducted only during working hours.
Response bias
Anything in a survey design that influences responses falls under the heading of response bias. One typical response bias, arises from the wording of questions, which may suggest a favored response.
Ex. Voters are more likely to express support of “the president” than support of the particular person holding the office at that moment.
Prospective study
An observational study in which subjects are followed to observe future outcomes. Because no treatments are deliberately applied, a prospective study is not an experiment. Nevertheless, prospective studies focus on estimating differences among groups that might appear as the groups are followed during the course of the study.
Experiment
An experiment manipulates factor levels to create treatments, randomly assign subjects to these treatment levels, and then compares the responses of the subject groups across treatment levels.
Random assignment
To be valid, an experiment must assign experimental units to treatment groups at random.
Factor
A variable whose values are manipulated by the experimenter. Experiments attempt to discover the effects that differences in factor levels may have on the responses of the experimental units.
Response
A variable whose values are compared across different treatments. In a randomized experiment, large response differences can be attributed to the effect of differences in treatment level.
Experimental units
Individuals on whom an experiment is performed. Usually called subjects or participants when they are human.
Level
The specific values that the experimenter chooses for a factor are called the levels of the factor.
Treatment
The process, intervention, or other controlled circumstance applied to randomly assigned experimental units. Treatments are the different levels of a single factor or are made up of combinations of levels of two or more factors.
Principles of experimental design
- Control aspects of the experiment that we know may have an effect on the response, but that are not the factors being studied.
- Randomize subjects to treatments to even out effects that we cannot control.
- Replicate over as many subjects as possible. Results for a single subject are just anecdotes. If, as often happens, the subjects of the experiment are not a representative sample from the population of interest, replicate the entire study with a different group of subjects, preferably from a different part of the population.
- Block to reduce the effects of identifiable attributes of the subjects that cannot be controlled.
Completely randomized design
In a completely randomized design, all experimental units have an equal chance of receiving any treatment.
Statistically significant
When an observed difference is too large for us to believe that it is likely to have occurred naturally, we consider the difference to be statistically significant. Subsequent chapters will show specific calculations and give rules, but the principle remains the same.
Control group
The experimental units assigned to a baseline treatment level, typically either the default treatment, which is well understood, or a null, placebo treatment. Their responses provide a basis for comparison.
Blinding
Any individual associated with an experiment who is not aware of how subjects have been allocated to treatment groups is said to be blinded.
Single blind and double blind
There are two main classes of individuals who can effect the outcome of an experiment:
-those who could influence the results (subjects, treatment administrators, technicians)
- those who evaluate the results (judges, treating physicians)
When every individual in either of these classes is blinded, an experiment is said to be single blind. When everyone in both classes is blinded, we call the experiment double blind.
Placebo
A treatment known to have no effect, administered to one group so that all groups experience the same conditions. Many subjects respond to such a treatment (a response called the placebo effect). Only by comparing with a placebo can we be sure that the observed effect of a treatment is not due simply to the placebo effect.
Placebo effect
The tendency of many human subjects (often 20% or more of experiment subjects) to show a response even when administered a placebo.
Blocking
When groups of experimental units are similar, it is often a good idea to gather them together into blocks. By blocking, we isolate the variability attributable to the differences between the blocks so that we can see the differences caused by the treatments more clearly.
Randomized block design
Subjects are randomly assigned to treatments within blocks.
Matching
In a retrospective or prospective study, subjects who are similar in ways not under study may be matched and then compared with each other on the variables of interest. Matching, like blocking, reduces unwanted variation.
Confounding
When the levels of one factor are associated with the levels of another factor in such a way that their effects cannot be separated, we say that these two factors are confounded.
