which one of the following indicates how free a measure is from random error?
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Validity identifies how free a measurement is from random error.
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Random assignment theoretically protects against unknown dissimilarities between experimental and control groups, particularly when the groups are large. Matching does not. The method only controls for known variables used in matching and any unknown variables that are highly correlated with them.
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The standard error (SE) of a statistic (usually an estimate of a parameter) is the standard deviation of its sampling distribution or an estimate of that standard deviation. ... Mathematically, the variance of the sampling distribution obtained is equal to the variance of the population divided by the sample size.
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Using the formula: {SEM = So x Sqroot(1-r)} where So is the Observed Standard Deviation and r is the Reliability the result is the Standard Error of Measurement(SEM). This gives an estimate of the amount of error in the test from statistics that are readily available from any test.
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Random error is always present in a measurement. ... Systematic errors are caused by imperfect calibration of measurement instruments or imperfect methods of observation, or interference of the environment with the measurement process, and always affect the results of an experiment in a predictable direction.
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Step 1: Calculate the mean (Total of all samples divided by the number of samples). Step 2: Calculate each measurement's deviation from the mean (Mean minus the individual measurement). ... Step 7: Divide the standard deviation by the square root of the sample size (n). That gives you the “standard error”.
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Systematic error or bias refers to deviations that are not due to chance alone. The simplest example occurs with a measuring device that is improperly calibrated so that it consistently overestimates (or underestimates) the measurements by X units. ... In fact, bias can be large enough to invalidate any conclusions.
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error variance. the element of variability in a score that is produced by extraneous factors, such as measurement imprecision, and is not attributable to the independent variable or other controlled experimental manipulations. ... Also called residual error; residual variance; unexplained variance.
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The accuracy of a measurement or approximation is the degree of closeness to the exact value. The error is the difference between the approximation and the exact value.
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Zero error can contain as many errors as you wish until the error occurs. This is not abstract as it seems. Uncertainty on the other hand can't contain any information of this sort as zero errors. If you can't tell exactly then, especially in non-linear systems, you have logical problem of sorting out errors.
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Percent Error. ... If the experimental value is less than the accepted value, the error is negative. If the experimental value is larger than the accepted value, the error is positive.
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The accuracy of a measurement is how close the measurement is to the true value of the quantity being measured. The accuracy of measurements is often reduced by systematic errors, which are difficult to detect even for experienced research workers.
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Definition: Random sampling is a part of the sampling technique in which each sample has an equal probability of being chosen. A sample chosen randomly is meant to be an unbiased representation of the total population.
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Accuracy of a measured value refers to how close a measurement is to the correct value. The uncertainty in a measurement is an estimate of the amount by which the measurement result may differ from this value. Precision of measured values refers to how close the agreement is between repeated measurements.
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Interpreting the statistic When the statistic is <1, then unsystematic variance (the denominator) is greater than the systematic variance, which is usually bad news, as it means you 'can't see the wood for the trees' as random effects swamp intended ones.
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Estimate. Since the population standard deviation is seldom known, the standard error of the mean is usually estimated as the sample standard deviation divided by the square root of the sample size (assuming statistical independence of the values in the sample). n is the size (number of observations) of the sample.
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The sampling error is the difference between a sample statistic used to estimate a population parameter and the actual but unknown value of the parameter. ... Sampling error also refers more broadly to this phenomenon of random sampling variation.