Sampling and Surveys
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| 9348522062 | Bias | This occurs when the design of a statistical study consistently underestimates or consistently overestimates the value you want to know. | 0 | |
| 9348522063 | Census | A study that attempts to collect data from every individual in the population. | 1 | |
| 9348522064 | Cluster sample | First divide the population into smaller groups. Ideally, these groups should mirror the characteristics of the population. Then choose an SRS of the groups. All individuals in the chosen groups are included in the sample. | 2 | |
| 9348522065 | Convenience sample | A sample selected by taking the members of the population that are easiest to reach; particularly prone to large bias. | 3 | |
| 9348522066 | Double-blind | An experiment in which neither the subjects nor those who interact with them and measure the response variable know which treatment a subject received. | 4 | |
| 9348522067 | Margin of error | A numerical estimate of how far the sample result is likely to be from the truth about the population due to sampling variability. | 5 | |
| 9348522068 | Nonresponse | Occurs when a selected individual cannot be contacted or refuses to cooperate. | 6 | |
| 9348522070 | Population | The entire group of individuals about which we want information. | 7 | |
| 9348522071 | Random sampling | The use of chance to select a sample; is the central principle of statistical sampling. | 8 | |
| 9348522072 | Response bias | In survey sampling, this refers to the bias that results from problems in the measurement process. | 9 | |
| 9348522073 | Sample | The part of the population from which we actually collect information. We use information from this group to draw conclusions about the entire population. | 10 | |
| 9348522074 | Sample survey | A study that uses an organized plan to choose a sample that represents some specific population. We base conclusions about the population on data from the sample. You must first say exactly what population you want to describe and second say exactly what you want to measure. | 11 | |
| 9348522075 | Sampling frame | The list, i.e. the subset of the population from which a sample is actually chosen. | 12 | |
| 9348522076 | Simple random sample (SRS) | A random sampling method. The method gives every possible sample of a given size the same chance to be chosen. | 13 | |
| 9348522077 | Strata | Groups of individuals in a population that are similar in some way that might affect their responses. | 14 | |
| 9348522078 | Stratified random sample | To select this sample, first classify the population into groups of similar individuals. Then choose a separate simple random sample from each group to form the full sample. | 15 | |
| 9348522079 | Table of random digits | A long string of the digits 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 with these properties: • Each entry in the table is equally likely to be any of the 10 digits 0 through 9. • The entries are independent of each other. That is, knowledge of one part of the table gives no information about any other part. | 16 | |
| 9348522080 | Undercoverage | Occurs when some members of the population are left out of the sampling frame; a type of sampling error. | 17 | |
| 9348522081 | Voluntary response samples | People decide whether to join a sample based on an open invitation; particularly prone to large bias. | 18 | |
| 9348522083 | Block | A group of experimental units that are known before the experiment to be similar in some way that is expected to affect the response to the treatments. | 19 | |
| 9348522084 | Completely randomized design | When the treatments are assigned to all the experimental units completely by chance. | 20 | |
| 9348522085 | Confounding | When two variables are associated in such a way that their effects on a response variable cannot be distinguished from each other. | 21 | |
| 9348522086 | Control group | An experimental group whose primary purpose is to provide a baseline for comparing the effects of the other treatments. Depending on the purpose of the experiment, this group may be given a placebo or an active treatment. | 22 | |
| 9348522087 | Double-blind | An experiment in which neither the subjects nor those who interact with them and measure the response variable know which treatment a subject received. | 23 | |
| 9348522088 | Experiment | Deliberately imposes some treatment on individuals to measure their responses. | 24 | |
| 9348522089 | Experimental units | The smallest collection of individuals to which treatments are applied. | 25 | |
| 9348522090 | Explanatory variable | A variable that helps explain or influences changes in a response variable. Also called factors. | 26 | |
| 9348522091 | Level | A specific value of an explanatory variable or factor in an experiment. For example, if we were studying effects of advertising an explanatory variable might be lengths of commercials and we could have commercials of varying lengths. Thirty, Forty-five and Sixty minute commercials would make three values of that one explanatory variable. | 27 | |
| 9348522092 | Matched pair | A common form of blocking for comparing just two treatments. In some such designs, each subject receives both treatments in a random order. In others, the subjects are paired with another as closely as possible, and each subject in a pair is randomly assigned to receive one of the treatments. | 28 | |
| 9348522093 | Observational study | Observes individuals and measures variables of interest but does not attempt to influence the responses. | 29 | |
| 9348522094 | Placebo | An inactive (fake) treatment. | 30 | |
| 9348522095 | Placebo effect | Describes the fact that some subjects respond favorably to any treatment, even an inactive one (placebo). | 31 | |
| 9348522096 | Random assignment | An important experimental design principle. Use some chance process to assign experimental units to treatments. This helps create roughly equivalent groups of experimental units at the start of the experiment. | 32 | |
| 9348522097 | Randomized block design | Start by forming groups consisting of individuals that are similar in some way that is important to the response. Random assignment of treatments is then carried out separately within each group. | 33 | |
| 9348522098 | Replication | An important experimental design principle. Use enough experimental units in each group so that any differences in the effects of the treatments can be distinguished from chance differences between the groups. | 34 | |
| 9348522099 | Response variable | A variable that measures an outcome of a study. | 35 | |
| 9348522100 | Single-blind | An experiment in which either the subjects or those who interact with them and measure the response variable, but not both, know which treatment a subject received. | 36 | |
| 9348522101 | Statistically significant | An observed effect so large that it would rarely occur by chance. | 37 | |
| 9348522102 | Subjects | Experimental units that are human beings. | 38 | |
| 9348522103 | Treatment | A specific condition applied to the individuals in an experiment. If an experiment has several explanatory variables, a this would be a combination of specific values of these variables. | 39 | |
| 9348522104 | Inference about cause and effect | Using the results of an experiment to conclude that the treatments caused the difference in responses. Requires a well-designed experiment in which the treatments are randomly assigned to the experimental units. | 40 | |
| 9348522105 | Inference about the population | Using information from a sample to draw conclusions about the larger population. Requires that the individuals taking part in a study be randomly selected from the population of interest. | 41 | |
| 9348522106 | Lack of realism | When the treatments, the subjects, or the environment of an experiment are not realistic. This can limit researchers' ability to apply the conclusions of an experiment to the settings of greatest interest. | 42 | |
| 9348522107 | Basic Principle for Designing Experiments | 1. Comparison - Use a design that compares two or more treatments. 2. Random Assignment - Use chance to assign experimental units. Create roughly equivalent groups of experimental units at the start of the experiment to balance the effects of other variables among the treatment groups. 3. Control - Keep other variables that might affect the response the same for all groups. (This is not the same as control group.) 4. Replication - Use enough experimental units in each group so the differences can be distinguished from chance. | 43 | |
| 9348522108 | Criteria for establishing causation when we can't do an experiment. | 1. The association is strong. 2. The association is consistent. 3. Larger values of the explanatory variable are associated with stronger responses. 4. The alleged cause precedes the effect in time. 5. The alleged cause is plausible. | 44 | |
| 9348522109 | Scope of Inference | 1. Inferences about populations are possible when individuals are randomly selected. 2. Inferences about cause and effect are possible when individuals are randomly assigned to groups. |  | 45 |
