Index: A-G | H-L | M-ZCategorical VariableA variable is categorical if its values fall into a distinct set of categories that do not overlap. For example, patient sex can take on the values of male or female. First treatment provided might have the values of 'IV line' and 'Airway inserted,' among others (also nominal). Show
Confidence IntervalsThe upper and lower boundaries that one is X percent sure the estimate falls within (as in 95% confidence limits). See Confidence Intervals in Advanced Statistical Topics. Continuous VariableA variable that can take on any value. For example height, weight, temperature, the amount of sugar in an orange, and the time required to run a mile are all continuous variables. Cross TabulationsComparing two or more variables of data. For example, you might want to see how many observations occur by age, gender, city, etc. Descriptive StatisticsStatistics used to summarize a body of data DispersionNumerical designations of how closely data cluster about the mean or other measure of central tendency. Frequency ChecksCreating a table that shows a body of data grouped according to numerical values. HistogramA bar graph representing a frequency distribution. Hypothesis TestingSee Hypothesis Testing in Advanced Statistical Topics. Independent SamplesIndependent samples are two or more samples selected from the same population, or different populations, that have no effect on one another. The outcome for one sample is assumed to be unrelated to the outcomes for each of the other samples. Or to restate the same principle, if you know the outcome for one sample, it will provide you with no information about the outcome for the other sample. Examples range from comparing males and females as two independent samples within a population to comparing a treatment group to a control group in an interventional study. Independent ObservationsTwo observations are independent if the occurrence of one observation provides no information about the occurrence of the other observation. A simple example is measuring the height of everyone in your sample at a single point in time. These should be unrelated observations. However, if you were to measure one child's height over time, these observations would be dependent because the height at each time point would affect the height at future time points. Independent VariableThe variable that causes or predicts the dependent variable. Also called the explanatory variable. Inferential StatisticsUsing sample statistics to infer characteristics about the population. Matched Pairs and Repeated MeasuresMatched samples can arise in the following situations:
Sometimes, the difference in the value of the measurement of interest for each matched pair is calculated, for example, the difference between before and after measurements, and these figures then form a single sample for an appropriate statistical analysis. Measures of CenterStatistics designed to represent the average or middle in a distribution of data. Minimum valueThe smallest observation in a set of data. Maximum valueThe largest observation in a set of data. MeanThe arithmetic average for a group of data. MedianThe middle item in a group of data when the data are ranked in order of magnitude. ModeThe most common value in any distribution. Normal DistributionA bell-shaped curve or distribution indicating that observations at or close to the mean occur with highest probability, and that the probability of occurrence progressively decreases as observations deviate from the mean. ObservationsData points in a given data set. Ordinal VariableAn ordinal variable has categories that can be ranked or ordered. However, the difference between levels may not be the same. For example, if you are administering a survey and ask the question, "How important do you think a primary seat belt law is?" you might have the following responses: 'Very important' 'Somewhat important' 'Not very important'. These responses have an obvious order, however, the difference between very important and somewhat important may not be the same as the difference between somewhat important and not very important. OutlierAn extreme value in a frequency distribution; can have a disproportionate influence on the mean. ParameterA measure used to summarize characteristics of a population based on all items in the population (such as a population mean). PopulationThe total set of items that one wants to analyze (all children, all citizens of a city, etc.). Probabilistic LinkageSee Probabilistic Linkage in Advanced Statistical Topics. ProbabilityExpresses the likelihood of a given even occurring over the long term. RandomEach subject has an equal chance of being selected. In research design, random assignement is a procedure that gives each subject an equal chance of placement in the experimental group or the control group so that now systematic difference exists between the groups prior to administration of the treatment. RangeA measure of dispersion calculated by subtracting the smallest value in a distribution from the largest value. Repeated MeasuresSee Matched Pairs and Repeated Measures. Response Variable (outcome, dependent)The variable that is caused or predicted by the independent variable. SampleA subset of the population usually selected randomly. Measures that summarize a sample are called sample statistics. SkewedData are considered skewed when most of the data values fall to the left or the right of the mean. SpreadHow spread out the data fall about the mean. Single Sample StatisticA single sample statistic is one in which you are only interested in describing one population. You are not interested in comparing different sub-populations within the single sample. For example, you want to describe the intubation rate for all pediatric patients. Standard DeviationA measure of dispersion; the square root of the average squared deviation from the mean. See Standard Deviation under Basic Statistics. StatisticA measure that is used to summarize a sample of data. Survival OutcomeSurvival refers to time-to-event data. For example, the time from an injury to normal functioning or the time from onset of disease to death. An important characteristic of survival outcomes is that they generally include censored observations, cases in which you do not observe the outcome of interest because either the study ends, people move, are discharged from the hospital, or are otherwise lost to follow up before their outcome occurs. Instead of these data being excluded, survival analysis can use censored observations in the analysis of your outcome. Target PopulationThe population a given study is intended to reach. VarianceThe average squared deviation from the mean; the square of the standard deviation. rev. 04-Aug-2022 What is a collection of measurements or observations?Data collection is a systematic process of gathering observations or measurements.
What is a collection of observations on the variables called?Data. Collections of observations (such as measurements, genders, survey response) Statistics.
What are measurements from a population called?A descriptive measure for an entire population is a ''parameter. '' There are many population parameters. For example, the population size (N) is one parameter, and the mean diastolic blood pressure or the mean body weight of a population would be other parameters that relate to continuous variables.
What is the complete collection of all measurements or data collected?A POPULATION is the complete collection of all measurements or data collected, whereas, a SAMPLE is a subcollection of members selected from the complete collection.
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