Random Error Epidemiology
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are three primary challenges to achieving an accurate estimate of the association: Bias Confounding, and Random error. Random error occurs random error vs systematic error epidemiology because the estimates we produce are based on samples, and samples may systematic error example not accurately reflect what is really going on in the population at large. . There are differences of opinion randomness error examples in decision making among various disciplines regarding how to conceptualize and evaluate random error. In this module the focus will be on evaluating the precision of the estimates obtained from samples. Learning Objectives
Chance In Epidemiology
After successfully completing this unit, the student will be able to: Explain the effects of sample size on the precision of an estimate Define and interpret 95% confidence intervals for measures of frequency and measures of association Define and interpret p-values Discuss common mistakes in the interpretation of measures of random error Random Error Consider two examples in which samples are random error calculation to be used to estimate some parameter in a population: Suppose I wish to estimate the mean weight of the freshman class entering Boston University in the fall, and I select the first five freshmen who agree to be weighed. Their mean weight is 153 pounds. Is this an accurate estimate of the mean value for the entire freshman class? Intuitively, you know that the estimate might be off by a considerable amount, because the sample size is very small and may not be representative of the mean for the entire class. In addition, if I were to repeat this process and take multiple samples of five students and compute the mean for each of these samples, I would likely find that the estimates varied from one another by quite a bit. This also implies that some of the estimates are very inaccurate, i.e. far from the true mean for the class. Suppose I have a box of colored marbles and I want you to estimate the proportion of blue marbles without looking into the box. I shake up the box and allow you t
of the measurement device. Random errors usually result from the experimenter's inability to take the same measurement in exactly random error examples physics 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
How To Reduce Systematic Error
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 http://sphweb.bumc.bu.edu/otlt/MPH-Modules/EP/EP713_RandomError/EP713_RandomError_print.html 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 https://www2.southeastern.edu/Academics/Faculty/rallain/plab193/labinfo/Error_Analysis/05_Random_vs_Systematic.html 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?
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 http://community.medchrome.com/2011/06/errors-and-bias-in-epidemiological.html a phenomenon in which the 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 random error of non-comparable records Error of measurement Random error/ Chance 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 how to reduce drawn randomly may contain: 0% diseased people 10% diseased people 70% diseased people This is 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 et