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| a prediction about the POPULATION based on results from the sample of that population | statistical inference
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| every member of a defined group being studied | population
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| a subset of the population being studied | sample
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| any characteristic that is oserved or measured (i.e. sex, weight, fasting blooding glucose, etc) | variable
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| the outcome of interest (blood pressure) | dependent variable
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| the intervention (blook pressure medication) | independent variable
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| variable (other than the independent variable) that can affect the dependent variable | confounding variable
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| the simplest form of measurement would be | nominal data
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| data consisting of varibles that can be sorted into one of a finite number of exlusive categories but NO VALUE or importance is assigned to the categories | nominal data
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| typically involves counting the number of obsevations in each category (like how many students in the class have asthmas and you count 25 do 95 don't) | nominal data
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| categories ARE given valueand CAN be ranked or ordered but eh distance b/w the categories is not quantifiable or constant like on a feeling scale or pain scale | ordinal data
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| vaiable are given value and can be ranked. the distance between intervals of measurement are quantifiable and constant. it has a arbitrary zero ( can have negative measurement); temperature | interval data
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| like interval data but has a absolute zero value (cannot be a negative measurement | ratio data (example would be BP measurements for patients taking HCTZ
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| statistical caculations that describe a set of data. The include "measures of central tendency" and Measures of dispersion" | descriptive statistics
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| the middle or average value around which other values fall | measures of central tendency found by calculating the mean (sum the data and divide by number of data given
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| the value where half of the data points fall above and half fall below when arranged in order. the median is less affected by outliers in the data. when even number of data give take the average of the two that fall in the middle | median
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| the most frequently occuring number | mode
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| an example of nominal data would be | mode
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| examples of ordinal data would be | mode and median
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| example of interval/ ration data would be | mode, median, and mean
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| on a pain scale it is not quantifiable so you can't find the mean | so it is not interval data or ratio data
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| if given a patients serum creatinine over the past week what could you find | mode, mean, and median so it is nominal, ordinal and interval but not ration because nott negative
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| if you counted the number of people in a clinic with diabeties by racial catergories what can you find | you only have numbers so you can find mode. the racial group with the most patients in it
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| when you measure how closely the data cluster around teh measure of central tendency is a measure of | dispersion or variability
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| the difference b/w the highest and lowest values of a set of data that is most affected by outliers is a | range
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| a statistical measure of how far a measurement is likey to lie from the mean measurement, it is the most widely used measure of dispersion, | standard devieation (sd)
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| when you state that there will be a difference between the test group and a group recieving treatment you are coming up with a | research hypothesis
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| when you say that the tested group will have no difference or that they are equal what kind of hypothesis is this | null hyppothes (example the number of seizure give with drug A is the same number that the patients will have if give drug B
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| what deals with the relative liklihood that a certain event will not occur | probability (hypothesis testing deals with probability because we want to know the probability that chance alone would yield the differences observed
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| the probability that chance alone would yield the difference observed in the samples is give by | p-value
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| what value gives you how much chance is considered acceptable, it is often arbitraily set at | 0.05 or 5% chance that the observed differences occurred by chance
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| what is defined as the ability of a study to reveal actual differences | power
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| what may not be observed or detected in a study if it doesn't posees enough power | differences
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| power is directly or indirectly related to sample size, effect size, predtermine alpha value | directly
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| what is an acceptable degree of power | 80%
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| when is the amount change tolerated adn the power of the study established before or after the study is performed | before during the planning stages of the study
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| it is easier for a study to reveal differences if the ____ size is large | effect
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| the p-value below which differences are considered statistically significant | alpha
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| which boolean opperator means together | AND both must appear in each citation retrieved
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| which boolena operator do you use to limit | and
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| which boolean operator do you use to broden | or
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| which boolean term should you use when only one of the terms must appear in each result | or
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| what does a scope note tell you | clearly helps you identify sysnomys
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