Largest Error In Science
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History Human Nature Live ScienceStrange News Oops! The 5 Greatest Scientific Blunders By Clara Moskowitz, LiveScience Senior Writer | May 16, 2013 07:25am ET MORE
Science Mistakes In History
Astrophysicist Mario Livio is the author of the new book "Brilliant Blunders" scientific mistakes that led to discoveries (May 2013, Simon & Schuster) Credit: Simon & Schuster/STScI Even geniuses make mistakes, and sometimes those mistakes turn out examples of experimental errors to be genius in their own right, helping to illuminate some underlying mystery or impacting the way an entire field thinks. In celebration of happy accidents and enlightening errors, astrophysicist
Scientific Errors Definition
Mario Livio of the Space Telescope Science Institute in Baltimore, Md., tells the stories of five great scientific mistakes in his new book "Brilliant Blunders" (Simon & Schuster, May 14, 2013). These stories serve to show how even the smartest among us can err, and that in fact to achieve a big breakthrough, big risks are necessary, which sometimes also involve big
Sources Of Error In Experiments
failures. Below are Livio's choices for the most brilliant scientific blunders. [Oops! 5 Retracted Science Studies] Darwin's notion of heredity Charles Darwin achieved an amazing feat when he came up with his theory of natural selection in 1859. In his new book "Brilliant Blunders," (May 2013, Simon & Schuster) astrophysicist Mario Livio details five famous scientific mistakes. Credit: Simon & Schuster "Darwin was an incredible genius," Livio told LiveScience. "His idea of evolution by natural selection is just mind-boggling — how he came up with something so all-encompassing as that. Plus Darwin really didn't know any mathematics so his theory is entirely non mathematical." This feat is even more incredible given the notion of heredity (how traits are passed from parents to offspring) that Darwin and scientists of the time subscribed to would have made natural selection impossible. At the time, people thought the characteristics of the mother and the father simply get blended in the offspring just as a can of black paint and a can of white paint blend to create gray when combined. Darwin's error was in not re
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Types Of Errors In Experiments
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purpose of this section is to explain how and why the results deviate from the expectations. Error analysis should include a calculation of how much the results vary from expectations. This http://sciencefair.math.iit.edu/writing/error/ can be done by calculating the percent error observed in the experiment. http://www.ahsd.org/science/stroyan/hphys/stats/meas_uncert_1.htm Percent Error = 100 x (Observed- Expected)/Expected Observed = Average of experimental values observed Expected = The value that was expected based on hypothesis The error analysis should then mention sources of error that explain why your results and your expectations differ. Sources of error must be specific. "Manual error" error in or "human error" are not acceptable sources of error as they do not specify exactly what is causing the variations. Instead, one must discuss the systematic errors in the procedure (see below) to explain such sources of error in a more rigorous way. Once you have identified the sources of error, you must explain how they affected your results. Did they make your largest error in experimental values increase or decrease. Why? One can classify these source of error into one of two types: 1) systematic error, and 2) random error. Systematic Error Systematic errors result from flaws in the procedure. Consider the Battery testing experiment where the lifetime of a battery is determined by measuring the amount of time it takes for the battery to die. A flaw in the procedure would be testing the batteries on different electronic devices in repeated trials. Because different devices take in different amounts of electricity, the measured time it would take for a battery to die would be different in each trial, resulting in error. Because systematic errors result from flaws inherent in the procedure, they can be eliminated by recognizing such flaws and correcting them in the future. Random Error Random errors result from our limitations in making measurements necessary for our experiment. All measuring instruments are limited by how precise they are. The precision of an instrument refers to the smallest difference between two quantities that the instrument can recognize. For example, the smallest markings on a normal metric ruler are separated by
the measurement devices (hard to read scales, etc.) - Usually caused by poorly or miscalibrated instruments. - There are usually ways to determine or estimate. - Cannot reduce by repeated measurements, but can account for in some way. 3. Indeterminate (Random) Errors
- Natural variations in measurements. - May be result of operator bias, variation in experimental conditions, or other factors not easily accounted for. - May be minimized by repeated measurement and using an average value. Experimental results may be described in terms of precision and accuracy. Precision - relatively low indeterminate error. - reproducibility. - high precision means a number of readings or trials result in values close to the same number. Accuracy - relatively low determinate error. - close to a true value. Accurate and precise Precise but not accurate Reliability- a procedure is said to be reliable if it may be completed with a high degree of accuracy and precision. For most of our investigations we will be concerned with the precision of results. Experimental Data and Measures of Uncertainty Quantities that give some measure of experimental precision are Deviation (individual values) Average deviation Average Deviation of the Mean (Standard Average Deviation) Sample standard deviation (sometimes denoted as ) Standard error It is customary to report experimental results with an uncertainty in the following form Result = Average ± uncertainty The uncertainty is one of the measures of precision given above (a.d., A.D., s, or Sx). For our present cases we will use standard error and report results as Result = Average ± Sx This information is simply preliminary to analyses we will be performing on some sample data, and data we will collect in the future. The idea here is to give you the formulae that are used to describe the precision of a set of data. We will see a bit more later. We need to see a calculation of these quantities. These pages illustrate one run through of calculations Another document will be about what these statistical quantities might tell us and how we might use this information to make certain decisions (usually as concerns elimination of data.) Reading Instruments and Errors Recorded values should reflect the precision of an instrument. Recorded values should have at least one more place than the smallest division on the scale of the instrument. Readings from a meter stick with major divis