Non Normal Distribution Standard Error
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Standard Deviation Skewed Distribution
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Real Life Examples Of Non Normal Distribution
it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top What does standard deviation tell us in non-normal distribution up vote 16 down vote favorite 6 In a normal distribution, the 68-95-99.7 rule imparts standard deviation a lot of meaning. But
Right Skewed Distribution
what would standard deviation mean in a non-normal distribution (multimodal or skewed)? Would all data values still fall within 3 standard deviations? Do we have rules like the 68-95-99.7 one for non-normal distributions? normal-distribution standard-deviation share|improve this question asked Jul 20 '14 at 7:54 Zuhaib Ali 181115 8 Have a look at Chebyshev's inequality. –COOLSerdash Jul 20 '14 at 8:08 @COOLSerdash great. This perfectly answers my question. –Zuhaib Ali Jul 20 '14 at 8:38 1 @COOLSerdash's point is on-target here, but be aware that the standard statement of Chebyshev's inequality pertains to the true SD known a-priori, not an SD estimated from your sample. It may help to read this excellent CV thread: Does a sample version of the one-sided Chebeshev inequality exist? –gung Jul 20 '14 at 16:15 Also, you should probably not settle for Chebyshev right away--you can probably do a lot better, skewed or not. –Steve S Jul 20 '14 at 16:34 I'm not interested in specifics... just need to understand what purpose does the SD concept univers
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I measured cytokines through ELISA in humans, but my values were very skewed since I got huge standard deviation which was nearly 2 fold larger than my mean value. Shall I use mean+/-standard error instead https://www.researchgate.net/post/Can_we_use_mean-standard_error_instead_of_mean-standard_deviation_when_data_is_non-parametric of mean+/-standard deviation when data is non-parametric? Topics Public Health × 690 Questions 115,166 https://en.wikipedia.org/wiki/Standard_error Followers Follow Epidemiology × 459 Questions 81,680 Followers Follow E-Learning for Epidemiology & Statistics × 45 Questions 8,878 Followers Follow Infectious Disease Epidemiology × 121 Questions 7,197 Followers Follow Obesity Epidemic × 104 Questions 17,466 Followers Follow Epidemiology and Public Health × 626 Questions 33,794 Followers Follow Aug 15, 2013 Share Facebook Twitter LinkedIn Google+ 2 normal distribution / 0 Popular Answers Jason Leung · The Chinese University of Hong Kong If data is skewed, using mean +/- standard deviation or mean +/- standard error is not good, becasue mean is distorted. You may present median and interquartile range(IQR) or median and range (min, max) Aug 15, 2013 Peter Donald Griffiths · University of Southampton Measuring cytokines through ELISA is very much not my topic but may I make non normal distribution a note based on my understanding of basic stats. Sandro say SD and SE are the same measure. That's not quite true. He correctly identifies the relationship between the two. However the SD is a measure of variation in the data (or an estimate of the variation in the population based on the data at hand). The Standard Error is a measure of sampling variation - that is the extent to which sample means vary around the population mean. If you are trying to describe your data it is unlikely that this is what you want to use. As for the data being 'non-parametric' this is not really the best way of looking at it. The data is what the data is - with a variety of 'parameters'. Non parametric TESTS make fewer assumptions about the underlying parameters - specifically they don't assume normal distributions. Whatever the distribution of the data, you can calculate mean and SD as the descriptive statistics - there is nothing 'wrong' about it - provided of course that the data is not ordinal / nominal rendering the numbers themselves essentially meaningless . The question is rather whether or not these are helpful summary descriptions of central tendency and variation. If the data is hig
proportion of samples that would fall between 0, 1, 2, and 3 standard deviations above and below the actual value. The standard error (SE) is the standard deviation of the sampling distribution of a statistic,[1] most commonly of the mean. The term may also be used to refer to an estimate of that standard deviation, derived from a particular sample used to compute the estimate. For example, the sample mean is the usual estimator of a population mean. However, different samples drawn from that same population would in general have different values of the sample mean, so there is a distribution of sampled means (with its own mean and variance). The standard error of the mean (SEM) (i.e., of using the sample mean as a method of estimating the population mean) is the standard deviation of those sample means over all possible samples (of a given size) drawn from the population. Secondly, the standard error of the mean can refer to an estimate of that standard deviation, computed from the sample of data being analyzed at the time. In regression analysis, the term "standard error" is also used in the phrase standard error of the regression to mean the ordinary least squares estimate of the standard deviation of the underlying errors.[2][3] Contents 1 Introduction to the standard error 1.1 Standard error of the mean (SEM) 1.1.1 Sampling from a distribution with a large standard deviation 1.1.2 Sampling from a distribution with a small standard deviation 1.1.3 Larger sample sizes give smaller standard errors 1.1.4 Using a sample to estimate the standard error 2 Standard error of the mean 3 Student approximation when σ value is unknown 4 Assumptions and usage 4.1 Standard error of mean versus standard deviation 5 Correction for finite population 6 Correction for correlation in the sample 7 Relative standard error 8 See also 9 References Introduction to the standard error[edit] The standard error is a quantitative measure of uncertainty. Consider the following scenarios. Scenario 1. For an upcoming national election, 2000 voters are chosen at random and asked if they will vote for candidate A or candidate B. Of the 2000 voters, 1040 (52%) state that they will vote for candidate A. The researchers report that candidate A is expected to receive 52% of the final vote, with a margin of error of 2%. In this scenario, the 2000 voters are a sample from all the actual voters. The sample proportion of 52% is an estimate of the true proportion who will vote for candidate A in