What is a statistical anomaly?
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Just enter "statistical anomaly" in the search bar on the Yahoo home page for a real answer.
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Try this website for help with your statistics homework:\n\nhttp://www.statsoft.com/textbook/stathome.html
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Engineering Statistics is based on the fact that everything can be explained as either part of a larger population or it is not.\nIf it is part of the population, the hypothesis test is true. If it is not part of the population the hypothesis is false and there must be an assignable cause.\n\nEngineering is always looking for inprovement. Using stats helps to explain variables and interactions affecting the outcome of an event.\n\nThere are other tools to accomplish this task, i.e. Run Charts, Scatter Diagrams, Taguchi Experiments, Design of Experiments, XbarR, Value Mapping ect.\n\nRead up on Deming, Joiner, Juran\n\nLook at the following Gurus list:
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No. This is what we call probability. Take a statistics course.
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The width of the box (more technically called the interquartile range) is used to define the maximum length of the lines (whiskers) that come out from the box.  That max length is 1.5 times the box width.  Anything beyond that is deemed to be an outlier, and shown as an individual point.\n\nThe wikipedia writeup on boxplots has a nice graphical example, I suggest you try the link below.  It shows both "mild" and "extreme" outliers.
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coincidental with what?
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This is a Two Tailed test ... in other words, you are trying to determine if you have statistical evidence to suggest that the average change was something other than zero disruptions different between weeks 1 and 2 ... just because you saw a change of 5, doesn't mean anything ... it could just be a random fluke !\n\nHo (Null Hypothesis):  Sample Aveage change in distruptions was zero -- no change, or X-Bar = 0\nHa (alternative):  Sample Average change was not 0, or X-Bar <> 0\n\nAt the 0.05 (5%) level of significance (in total), you would have 0.025 probability in the upper end of the distribution and another 0.025 probability in the other end ... that's a total of 0.05 or 5%\n\nUsing your Z-Score table, you find the Z-Score (the number of Standard Deviations in a Standardized Normal Distribution with mean of 0 and Standard Deviation of 1) associated with 0.025 or 2.5% ... this is:\n\n-1.959961082 Standard Deviations from the mean for the low tail and 1.959961082 standard deviations from the mean for the high tail.\n\nSo, the number of standard deviations from a presumed mean of zero (under the null hypothesis) for the sample at hand is:\n\n( 5 - 0 ) / 2  = 2.5\n\nThat is to say, that if the Null Hypothesis of NO CHANGE in behavior were true, a legitimate value of 5 drawn purely randomly could still happen, but that would be 2.5 Standard Deviations away.\n\nBut, your upper and lower limits were only +/- 1.959961082 !!!\n\nSo 95% of the time, you would expect to see something between +/- 1.959961082 standard deviations from zero (within this range) ... but this time, you saw a value associated with a whopping 2.5 standard deviations from the presumed mean of zero ... (we'll call 1.959961082 = 1.96 here)\n\n<< I ACTUALLY MADE A LITTLE GRAPHIC HERE, BUT IT WON'T DISPLAY PROPERLY >>\n\nSo you would reject Ho.\n\nConclusion:  Based upon the sample taken, beleieved to be truely random and under the assumptions of normality, we have statistical evidience which would tend to suggest that the observed sample difference of 5 is statistically significant at the 95% confidence level, thereby, leading us to conclude, based upon the evidence observed, that the incentive system may have played a roll in the positive inprovement of the respondents.\n\nDoes this help ?   My contact information is in my profile.\n\nRegards,\n\nAlan
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In data and signal analysis, an apparent trend or signal caused by random events or noise is often called spurious.
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the correct spelling is "anomalous"\nyou can loook it up in dictionary.com to confirm
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Statistics studies, among other things, deviations from normals and can help to explain errors (as well as help explain how errors are propagated and where their sources can be).\n\nFor example, if you have a numerical amount with an error and you multiply that value by something known and constant (like conversion from USD to Euros, for example), that error will be multiplied by that conversion rate.\n\nStatistics uses error calculation such as standard deviation (how much a value can vary from the average amount) and goes very far into detail about sums of errors etc. and how they affect averages, errors, and final values.\n\nAdditinoally, there are some formulae that are used all throughout the sciences that can be applied to accounting, and those sciences use statistics, again, to calculate errors.  For example, growth and decay of a radioactive substance has a certain formula that uses exponential (e to the power of something), but this is also used to calculate economic growth rates and can be applied to the stock market (it can also be used to explain traffic jams and other civil engineering problems).
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why would it be abnormal?
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When the population standard deviation is unknown, thats when you use the t-distribution, in calculating the confidence interval. \n\nThe t-distribution (also known as Student's t-distribution) measures the significance of a difference of means of small samples when two distributions are though to have the same variance, but possibly different means.\n\nBut as the sample size increases, the t-distribution gets close to the normal distribution.\n\nHope this helps!
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what type of statistics?
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http://en.wikipedia.org/wiki/Mean_%28statistics%29\n\nThis is a link from Wikipedia. It shows lots of examples along with explanations.
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Would seem to mean that there is an error somewhere that needs attention.  Maybe a change to your system has been detected and needs your attention!
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The previous answer is correct.  Also, if the standard deviation is greater than your mean, then that may indicate your data has a large range, and most likely have an outlier.  For example, if you data is {1,1,1,100}.\nThe mean is 25.75\nThe standard deviation is 49.5.\nThe range of data is 100-1=99, with the outlier being 100.\n\nSometimes, experimentalist would throw out the outlier data and recompute statistics for the new set of data.  So if we throw out the outlier 100 and can rationalize that 100 is a corrupted data point because our previous trials were all 1's, then our new data set is {1,1,1} and\nThe mean is 1\nThe standard deviation is 0.\nThe range of data is 1-1=0.
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statistics are calculated for one reason: to persuade someone. for example the statistics of teen pregnancy are used to persuade teenage girls not to get pregnant.