I believe the above is a reductionistic assumption bassed upon ill-informed logic. Chi-square is a statistic that is related to the central limit theorem in the sense that proportions are in fact means, and that proportions are normally distributed (with a mean of pi [not 3.141592653...] and a variance of pi*(1-pi)). Therefore, we can perform a normal curve test for examining the difference between proportions such that Z squared = chi square on one degree of freedom. Since Z is indubitably a parametric test, and chi square can be related to Z, we can infer that it is, in fact, parametric.
From another approach, a parametric test is a test that makes an assumption about the value of a parameter (the measure of the population rather than your sample) in a statistical density function. Since our expected frequencies are based upon either theory, or a mathematical assumption based upon the average of our presented frequencies, i.e. the mean, we are making an assumption about what the parameter of our distribution would be. Therefore, given this assumption, and the relationship of chi square to the normal curve, one can argue for chi square being a parametric test.
If the distribution is parametric then yes.
A classic would be the Kolmogorov-Smirnov test.
A paired samples t-test is an example of parametric (not nonparametric) tests.
Parametric statistical tests assume that your data are normally distributed (follow a classic bell-shaped curve). An example of a parametric statistical test is the Student's t-test.Non-parametric tests make no such assumption. An example of a non-parametric statistical test is the Sign Test.
log 10 or square root your non parametric values
definition of nonparametric equestion?and give exampls?
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.
1. A nonparametric statistic has no inference 2. A nonparametric statistic has no standard error 3. A nonparametric statistic is an element in a base population (universe of possibilities) where every possible event in the population is known and can be characterized * * * * * That is utter rubbish and a totally irresponsible answer. 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. With non-parametric data you can compare between two (or more) possible distributions (goodness-of-fit), test for correlation between variables. Some test, such as the Student's t, chi-square are applicable for parametric as well as non-parametric statistics. I have, therefore, no idea where the previous answerer got his/her information from!
You might be referring to parametric vs nonparametric methods.
David Sheskin has written: 'Handbook of parametric and nonparametric statistical procedures' -- subject(s): Mathematical statistics, Handbooks, manuals 'Handbook of parametric and nonparametric statistical procedures' -- subject(s): Mathematical statistics, Handbooks, manuals, etc, Handbooks, manuals
Yes, Chis squared test are among the most common nonparametric statistics tests.
c2 (Chi Square)
Parametric for one set?! Yeah
The symbol for hypothesis test is c2 ( Chi Square)
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.
* 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.
It is not.It is not.It is not.It is not.
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
Binomial is a non- parametric test. Since this binomial test of significance does not involve any parameter and therefore is non parametric in nature, the assumption that is made about the distribution in the parametric test is therefore not assumed in the binomial test of significance. In the binomial test of significance, it is assumed that the sample that has been drawn from some population is done by the process of random sampling. The sample on which the binomial test of significance is conducted by the researcher is therefore a random sample.
The Fisher F-test for Analysis of Variance (ANOVA).
When the variable that is being tested has an unknown distribution.