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If the distribution is parametric then yes.

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Q: Is parametric test stronger than nonparametric test?
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What is the difference between parametric and non parametric?

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.


Distingnish between parametric and nonparametric statistics. Why the parametric statistics are considered more powerful than the nonparametric statistics. Explain.?

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 &ge; k and H1: median < kRight tailed test: H0: median &le; k and H1: median > kTwo tailed test: H0: median &ne; 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&prime;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 &le; 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.


What are the advantages and disadvantages of nonparametric statistics compared to the parametric statistics?

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


Is 12LB fishing line stronger than 10LB fishing line?

Yes; 12 lb. test is stronger than 10 lb. test.


Is chi-square test parametric or non-parametric?

non-parametric 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.


Distinguish between parameteric statistics and non - parameteric statistics?

The simplest answer is that parametric statistics are based on numerical data from which descriptive statistics can be calculated, while non-parametric statistics are based on categorical data. Takes two example questions: 1) Do men live longer than women, and 2), are men or women more likely to be statisticians. In the first example, you can calculate the average life span of both men and women and then compare the two averages. This is a parametric test. But in the second, you cannot calculate an average between "man" and "woman" or between "statistician" or "non-statistician." As there is no numerical data to work with, this would be a non-parametric test. The difference is vitally important. Because inferential statistics require numerical data, it is possible to estimate how accurate a parametric test on a sample is compared to the relevant population. However, it is not possible to make this estimation with non-parametric statistics. So while non-parametric tests are still used in many studies, they are often regarded as less conclusive than parametric statistics. However, the ability to generalize sample results to a population is based on more than just inferential statistics. With careful adherence to accepted random sampling, sample size, and data collection conventions, non-parametric results can still be generalizable. It is just that the accuracy of that generalization can not be statistically verified.


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