01 parametric and non parametric statistics

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Definition of Parametric and Non-parametric Statistics Assumptions of Parametric and Non-parametric Statistics Assumptions of Parametric Statistics Assumptions of Non-parametric Statistics Advantages of Non-parametric Statistics Disadvantages of Non-parametric Statistical Tests Parametric Statistical Tests for Different Samples Parametric Statistical Measures for Calculating the Difference Between Means Significance of Difference Between the Means of Two Independent Large and Small Samples Significance of the Difference Between the Means of Two Dependent Samples Significance of the Difference Between the Means of Three or More Samples Parametric Statistics Measures Related to Pearson’s ‘r’ Non-parametric Tests Used for Inference

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

01 parametric and non parametric statistics

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The error (or disturbance) of an observed value is the deviation of the observed value from the (unobservable) true value of a quantity of interest (for example, a population mean), and the residual of an observed value is the difference between the observed value and the estimated value of the quantity of interest (for example, a sample mean). Suppose there is a series of observations from a univariate distribution and we want to estimate the mean of that distribution (the so-called location model). In this case, the errors are the deviations of the observations from the population mean, while the residuals are the deviations of the observations from the sample mean. A statistical error (or disturbance) is the amount by which an observation differs from its expected value, the latter being based on the whole population from which the statistical unit was chosen randomly. For example, if the mean height in a population of 21-year-old men is 1.75 meters, and one randomly chosen man is 1.80 meters tall, then the "error" is 0.05 meters; if the randomly chosen man is 1.70 meters tall, then the "error" is −0.05 meters. The expected value, being the mean of the entire population, is typically not observable, and hence the statistical error cannot be observed either.

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The error (or disturbance) of an observed value is the deviation of the observed value from the (unobservable) true value of a quantity of interest (for example, a population mean), and the residual of an observed value is the difference between the observed value and the estimated value of the quantity of interest (for example, a sample mean). Suppose there is a series of observations from a univariate distribution and we want to estimate the mean of that distribution (the so-called location model). In this case, the errors are the deviations of the observations from the population mean, while the residuals are the deviations of the observations from the sample mean. A statistical error (or disturbance) is the amount by which an observation differs from its expected value, the latter being based on the whole population from which the statistical unit was chosen randomly. For example, if the mean height in a population of 21-year-old men is 1.75 meters, and one randomly chosen man is 1.80 meters tall, then the "error" is 0.05 meters; if the randomly chosen man is 1.70 meters tall, then the "error" is −0.05 meters. The expected value, being the mean of the entire population, is typically not observable, and hence the statistical error cannot be observed either.

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