Random Error Vs Systematic Error Epidemiology
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of the measurement device. Random errors usually result from the experimenter's inability to take the same measurement in exactly
Random Error Examples
the same way to get exact the same number. Systematic how to reduce random error errors, by contrast, are reproducible inaccuracies that are consistently in the same direction. Systematic errors are systematic error calculation often due to a problem which persists throughout the entire experiment. Note that systematic and random errors refer to problems associated with making measurements. Mistakes made
How To Reduce Systematic Error
in the calculations or in reading the instrument are not considered in error analysis. It is assumed that the experimenters are careful and competent! How to minimize experimental error: some examples Type of Error Example How to minimize it Random errors You measure the mass of a ring three times using the same
Random Error Examples Physics
balance and get slightly different values: 17.46 g, 17.42 g, 17.44 g Take more data. Random errors can be evaluated through statistical analysis and can be reduced by averaging over a large number of observations. Systematic errors The cloth tape measure that you use to measure the length of an object had been stretched out from years of use. (As a result, all of your length measurements were too small.)The electronic scale you use reads 0.05 g too high for all your mass measurements (because it is improperly tared throughout your experiment). Systematic errors are difficult to detect and cannot be analyzed statistically, because all of the data is off in the same direction (either to high or too low). Spotting and correcting for systematic error takes a lot of care. How would you compensate for the incorrect results of using the stretched out tape measure? How would you correct the measurements from improperly tared scale?
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Zero Error Definition
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Epidemiological Studies 5:53 AM Sulav Shrestha 2 comments Email This BlogThis! Share to Twitter Share to Facebook Concept of Error: In epidemiology: refers to a phenomenon in which the http://community.medchrome.com/2011/06/errors-and-bias-in-epidemiological.html result or finding of the study does not reflect the truth of the fact. Types of Error: Random (chance) Error - associated with precision Systematic Error/Bias - associated with selection Common Sources of Error: Selection bias Absence or inadequacy of controls Unwarranted conclusion Ignoring the periods of exposure to risk Improper interpretation of associations Mixing of non-comparable records Error of measurement Random error/ Chance random error variation Error that generally occurs in sampling procedure. It is a divergence, due to chance alone, of an observation on a sample from the true population value, leading to lack of precision in the measurement of an association. Picture description: Out of a sample of 100 people, 3 consecutive sample drawn randomly may contain: 0% diseased people 10% diseased people 70% diseased people This is random error examples called random error where the error is due to chance. The only way to reduce it is to increase the size of sample. Elimination of error is not possible Sources of random error: Individual biological variation Sampling error Measurement error Types of Random Errors Type I Error - alpha error Type II Error - beta error How to reduce Random Error? Increase the size of the study. Systemic Error/Bias Any process or attempts in any stage of the study from designing to its execution to the application of information from the study which produces results or conclusions that differ systematically from truth. A. Selection Bias A distortion in true study finding due to improper selection procedures or it is due to an effect of selection process Most common type of bias. Some potential sources of selection biases: Self selection bias Selection of control group Selection of sampling frame Loss to follow up Improper diagnostic criteria More intensive interview to desired subjects etc. B. Information Bias It is distortion in true study finding due to improper information/lack of information or misclassification. Potential sources of Information Bias: Invalid instrument Incorrect diagnostic criteria Misclassifications
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