Random Error 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?
of causes of random errors are: electronic noise in the circuit of an electrical instrument, irregular changes in the heat loss rate from a solar collector due to changes in the wind. Random errors often have a Gaussian normal distribution (see Fig. 2). In such cases random error calculation statistical methods may be used to analyze the data. The mean m of a number zero error definition of measurements of the same quantity is the best estimate of that quantity, and the standard deviation s of the measurements shows the accuracy
Personal Error
of the estimate. The standard error of the estimate m is s/sqrt(n), where n is the number of measurements. Fig. 2. The Gaussian normal distribution. m = mean of measurements. s = standard deviation of measurements. 68% of the https://www2.southeastern.edu/Academics/Faculty/rallain/plab193/labinfo/Error_Analysis/05_Random_vs_Systematic.html measurements lie in the interval m - s < x < m + s; 95% lie within m - 2s < x < m + 2s; and 99.7% lie within m - 3s < x < m + 3s. The precision of a measurement is how close a number of measurements of the same quantity agree with each other. The precision is limited by the random errors. It may usually be determined by repeating the measurements. Systematic Errors Systematic http://www.physics.umd.edu/courses/Phys276/Hill/Information/Notes/ErrorAnalysis.html errors in experimental observations usually come from the measuring instruments. They may occur because: there is something wrong with the instrument or its data handling system, or because the instrument is wrongly used by the experimenter. Two types of systematic error can occur with instruments having a linear response: Offset or zero setting error in which the instrument does not read zero when the quantity to be measured is zero. Multiplier or scale factor error in which the instrument consistently reads changes in the quantity to be measured greater or less than the actual changes. These errors are shown in Fig. 1. Systematic errors also occur with non-linear instruments when the calibration of the instrument is not known correctly. Fig. 1. Systematic errors in a linear instrument (full line). Broken line shows response of an ideal instrument without error. Examples of systematic errors caused by the wrong use of instruments are: errors in measurements of temperature due to poor thermal contact between the thermometer and the substance whose temperature is to be found, errors in measurements of solar radiation because trees or buildings shade the radiometer. 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.
Taken from R. H. B. Exell, www.jgsee.kmutt.ac.th/exell/PracMath/ErrorAn.htmEpidemiological 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 Misc
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