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Q: What are examples of parametric and nonparametric statistical tests?

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Parametric tests draw conclusions based on the data that are drawn from populations that have certain distributions. Non-parametric tests draw fewer conclusions about the data set. The majority of elementary statistical methods are parametric because they generally have larger statistical outcomes. However, if the necessary conclusions cannot be drawn about a data set, non-parametric tests are then used.

Nonparametric tests are sometimes called distribution free statistics because they do not require that the data fit a normal distribution. Nonparametric tests require less restrictive assumptions about the data than parametric restrictions. We can perform the analysis of categorical and rank data using nonparametric tests.

A paired samples t-test is an example of parametric (not nonparametric) tests.

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Non-Parametric statistics are statistics where it is not assumed that the population fits any parametrized distributions. Non-Parametric statistics are typically applied to populations that take on a ranked order (such as movie reviews receiving one to four stars). The branch of http://www.answers.com/topic/statistics known as non-parametric statistics is concerned with non-parametric http://www.answers.com/topic/statistical-model and non-parametric http://www.answers.com/topic/statistical-hypothesis-testing. Non-parametric models differ from http://www.answers.com/topic/parametric-statistics-1 models in that the model structure is not specified a priori but is instead determined from data. The term nonparametric is not meant to imply that such models completely lack parameters but that the number and nature of the parameters are flexible and not fixed in advance. Nonparametric models are therefore also called distribution free or parameter-free. * A http://www.answers.com/topic/histogram is a simple nonparametric estimate of a probability distribution * http://www.answers.com/topic/kernel-density-estimation provides better estimates of the density than histograms. * http://www.answers.com/topic/nonparametric-regression and http://www.answers.com/topic/semiparametric-regression methods have been developed based on http://www.answers.com/topic/kernel-statistics, http://www.answers.com/topic/spline-mathematics, and http://www.answers.com/topic/wavelet. Non-parametric (or distribution-free) inferential statistical methodsare mathematical procedures for statistical hypothesis testing which, unlike http://www.answers.com/topic/parametric-statistics-1, make no assumptions about the http://www.answers.com/topic/frequency-distribution of the variables being assessed. The most frequently used tests include

There are several reasons, including the following, in no particular order:I suspect that many or most people learn the parametric alternatives first, or learn mainly the parameteric alternatives.When the correct conditions hold, the parametric alternatives provide the best power.In some situations, such as the more complicated ANOVA and related methods, there are no nonparametric alternatives.Often data that do not appear to satisfy the requirements for parametric procedures can be transformed so that they do, more or less.Parametric procedures have been shown to be robust in the face of departures from the assumptions on which they were based, in many cases.

* Always when the assumptions for the specific test (as there are many parametric tests) are fulfilled. * When you want to say something about a statistical parameter.

Fred L. Ramsey has written: 'Birding Oregon' -- subject(s): Bird watching 'A small sample study of some non-parametric tests of location' -- subject(s): Nonparametric statistics, Statistical hypothesis testing

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Parametric are the usual tests you learn about. Non-parametric tests are used when something is very "wrong" with your data--usually that they are very non-normally distributed, or N is very small. There are a variety of ways of approaching non-parametric statistics; often they involve either rank-ordering the data, or "Monte-Carlo" random sampling or exhaustive sampling from the data set. The whole idea with non-parametrics is that since you can't assume that the usual distribution holds (e.g., the X² distribution for the X² test, normal distribution for t-test, etc.), you use the calculated statistic but apply a new test to it based only on the data set itself.

What is DC parametric tests

Parametric tests assume that your data are normally distributed (i.e. follow a classic bell-shaped "Gaussian" curve). Non-parametric tests make no assumption about the shape of the distribution.

Yes, Chis squared test are among the most common nonparametric statistics tests.

Parametric statistical tests assume that the data belong to some type of probability distribution. The normal distribution is probably the most common. That is, when graphed, the data follow a "bell shaped curve".On the other hand, non-parametric statistical tests are often called distribution free tests since don't make any assumptions about the distribution of data. They are often used in place of parametric tests when one feels that the assumptions of the have been violated such as skewed data.For each parametric statistical test, there is one or more nonparametric tests. A one sample t-test allows us to test whether a sample mean (from a normally distributed interval variable) significantly differs from a hypothesized value. The nonparametric analog uses the One sample sign test In one sample sign test,we can compare the sample values to the a hypothesized median (not a mean). In other words we are testing a population median against a hypothesized value k. We set up the hypothesis so that + and - signs are the values of random variables having equal size. A data value is given a plus if it is greater than the hypothesized mean, a negative if it is less, and a zero if it is equal.he sign test for a population median can be left tailed, right tailed, or two tailed. The null and alternative hypothesis for each type of test will be one of the following:Left tailed test: H0: median ≥ k and H1: median < kRight tailed test: H0: median ≤ k and H1: median > kTwo tailed test: H0: median ≠ k and H1: median = kTo use the sign test, first compare each entry in the sample to the hypothesized median k.If the entry is below the median, assign it a - sign.If the entry is above the median, assign it a + sign.If the entry is equal to the median, assign it a 0.Then compare the number of + and - signs. The 0′s are ignored.If there is a large difference in the number of + and - signs, then it is likely that the median is different from the hypothesized value and the null hypothesis should be rejected.When using the sign test, the sample size n is the total number of + and - signs.If the sample size > 25, we use the standard normal distribution to find the critical values and we find the test statistic by plugging n and x into a formula that can be found on the link.When n ≤ 25, we find the test statistic x, by using the smaller number of + or - .So if we had 10 +'s and 5 -'s, the test statistic x would be 5. The zeros are ignored.I will provided a link to some nonparametric test that goes into more detail. The information about the Sign Test was just given as an example of one of the simplest nonparametric test so one can see how these tests work The Wilcoxon Rank Sum Test, The Mann-Whitney U test and the Kruskal-Wallis Test are a few more common nonparametric tests. Most statistics books will give you a list of the pros and cons of parametric vs noparametric tests.

There are about 30 statistical tests that can you run on SPSS.

The advantages of parametric tests include labeling individual distributions within a particular family. Each normal distribution is uniquely determined by its mean and standard deviation.

In a sense, and whether they realise it or not, thousands of researchers are using parametric modelling whenever they employ t-tests, F-tests, chi-square tests, or any of the myriad other tests in common use. All of these are based on parametric models.There is also a large class of scientists, including physicists, chemists, experimental psychologists, biologists, astronomers and others, that make heavy use of parametric models to describe systems that they have encountered.

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Typically, you will find some type of business statistical analysis, and/or tests and measurements.Typically, you will find some type of business statistical analysis, and/or tests and measurements.Typically, you will find some type of business statistical analysis, and/or tests and measurements.Typically, you will find some type of business statistical analysis, and/or tests and measurements.Typically, you will find some type of business statistical analysis, and/or tests and measurements.Typically, you will find some type of business statistical analysis, and/or tests and measurements.

In parametric analysis the underlying distributions of the variables are described by parameters. These may be known or it may be possible to estimate them from the observed data. In non-parametric analyses, the parameters are not used - either because they cannot be derived or because the tests do not require them.

In parametric statistics, the variable of interest is distributed according to some distribution that is determined by a small number of parameters. In non-parametric statistics there is no underlying parametric distribution. In both cases, it is possible to look at measures of central tendency (mean, for example) and spread (variance) and, based on these, to carry out tests and make inferences.

It is found under Analyze ---> Nonparametric Tests ---> 1 Sample K-S

These tests are called statistical tests.

These tests are called statistical tests.

Most parametric and many non-parametric tests for testing the significance of hypotheses are based on the assumption of normality - or approximate normality.