Standard Deviation Definition Standard Deviation Definition Variation (dispersion) is the property of deviation of values from the average. The degree of variation is indicated by the measures of variation. There are various measures of variation and the commonly used are 1) Range 2) Mean deviation 3) Standard Deviation and 4) Quartile deviation. The range is based only on the lowest and the highest values. Quartile deviation is based only on the quartiles, and not based on values. Mean deviation is based on values nut it is not convenient for mathematical analysis. So, we consider standard deviation which is based on all the values. The standard deviation of a set of values is the positive square root of mean of the standard deviations of the values from their arithmetic mean. It is denoted by Ďƒ (sigma). Measures of dispersion are statistical devices to measure the variability or dispersion in a series. They tell us the extent to which the values of the series differ between each other or from their average. Know More About Binomial Probability Formula
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Measures of Central tendency are average of original values so they are known as averages of first order. Measures of dispersion are only averages of deviations taken from the average. Therefore they are known as averages of second order. Standard Deviation Definition Standard Deviation is a measure of dispersion in statistics. It gives an idea of how the individual data in a dataset is dispersed fromthe mean. For example : The mean of 5 and 6 is 5.5. Also the mean of 1 and 10 is also 5.5 The data points 5 and 6 are closer to 5.5 than data points 1 and 10. This means standrad deviation of thedata set 1,10 is more than that of the data set 5,6. Thus standard deviation gives an idea of the dispersion of the data from the mean Related term is the variance. variance is the square of the stanadrd deviation Standard deviation is defined as the square root of the mean of the squares of the deviations of all the values of a series taken from the arithmetic mean. It is also known as root mean square deviation. The symbol used for standard deviation is Ďƒ 1. The minimum value of standard deviation is 0. i.e. it cannot be negative 2. When the items in a series are more dispersed from mean,, then Standard deviation is also large. Merits of standard deviation 1. It is based on all observation in a distribution 2. It is capable of further algebraic treatment. 3. We can find out measure like coefficient of variation, combined standard deviation extra. Demerits of Standard deviation 1. It is difficult to calculate 2. It gives more importance to bigger values. Learn More What is a Line Plot
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Definition of Bias Definition of Bias Bias is a term used very frequently in statistics and is used in different scenarios. Bias can be due to faulty collection of data. During the process of collecting the actual information in a survey certain inaccuracies may creep and these may cause bias. Bias can be seen during analysis. Faulty methods of analysis of data may also introduce bias. If possibilities of bias exist, the conclusions drawn from the sample cannot be regarded as fully objective. The first essential of any sampling or census procedure must therefore be elimination of all sources of bias. To avoid bias in the selection process is to draw the sample either entirely at random or at random subject to such restrictions, while improving the accuracy would not introduce bias into the results. Bias arising from substitution should not be allowed to enter the survey and bias arising from faulty collection of data may also be removed in number of ways. List of BiasBack to Top The following different types of bias are used in several concepts of statistics: * Spectrum Bias * Omitted- Variable Bias Math.Tutorvista.com
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* Systematic Bias * Cognitive Bias Different Types of Bias - Explained Spectrum bias contains the evaluating the capacity of a diagnostic test in a biased group of enduring, which guides to an overestimate of the sensitivity and specificity of the test. An unrecognized but very much similar to real problem is that of spectrum bias. This is the phenomenon of the sensitivity or specificity of a test varying with different populations tested populations which might vary in sex ratios, age, or severity of disease as three general examples. The bias of an estimator is the variation between an estimator's anticipation and the true value of the factor being estimated. Omitted-variable bias is the bias that shows in approximations of parameters in a regression analysis when the assumed specification is incorrect, in that it omits an independent variable that should be in the model. In statistics hypothesis testing, a test what is said to be unbiased when the probability of declining the null hypothesis exceeds the consequence level when the alternative is true and is less than or equal to the significance level when the null hypothesis is true. Systematic bias or systemic bias is external influences that may concern the accuracy of statistical measurements. Systemic bias is the inherent tendency of a process to favor the particular outcomes. The word is a nrologism that generally refers to human systems. The analogous problem in nonhuman systems is often called systematic and leads to systematic in measurements or estimates. Data-snooping bias gets from the abuse of data mining techniques. In statistics, one type of cognitive bias is confirmation bias, the propensity to interpret new information in what way that proves one's prior attitude, still to the severe of denial, ignoring information that differences with one's prior beliefs. The basic attribution error, also called the correspondence bias. Read More About What is a Bar Graph
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