# Short Notes On Parametric And Non Parametric Test In Pdf File Name: short notes on parametric and non parametric test in .zip
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Published: 11.05.2021  To make the generalisation about the population from the sample, statistical tests are used. A statistical test is a formal technique that relies on the probability distribution, for reaching the conclusion concerning the reasonableness of the hypothesis. These hypothetical testing related to differences are classified as parametric and nonparametric tests.

In statistics, parametric and nonparametric methodologies refer to those in which a set of data has a normal vs.

The three modules on hypothesis testing presented a number of tests of hypothesis for continuous, dichotomous and discrete outcomes. Tests for continuous outcomes focused on comparing means, while tests for dichotomous and discrete outcomes focused on comparing proportions. All of the tests presented in the modules on hypothesis testing are called parametric tests and are based on certain assumptions. For example, when running tests of hypothesis for means of continuous outcomes, all parametric tests assume that the outcome is approximately normally distributed in the population.

## Difference Between Parametric and Nonparametric Test

To make the generalisation about the population from the sample, statistical tests are used. A statistical test is a formal technique that relies on the probability distribution, for reaching the conclusion concerning the reasonableness of the hypothesis.

These hypothetical testing related to differences are classified as parametric and nonparametric tests. The parametric test is one which has information about the population parameter. So, take a full read of this article, to know the significant differences between parametric and nonparametric test.

Basis for Comparison Parametric Test Nonparametric Test Meaning A statistical test, in which specific assumptions are made about the population parameter is known as parametric test. A statistical test used in the case of non-metric independent variables, is called non-parametric test.

The parametric test is the hypothesis test which provides generalisations for making statements about the mean of the parent population. The t-statistic rests on the underlying assumption that there is the normal distribution of variable and the mean in known or assumed to be known.

The population variance is calculated for the sample. It is assumed that the variables of interest, in the population are measured on an interval scale. The nonparametric test is defined as the hypothesis test which is not based on underlying assumptions, i. The test is mainly based on differences in medians. Hence, it is alternately known as the distribution-free test. The test assumes that the variables are measured on a nominal or ordinal level.

It is used when the independent variables are non-metric. The fundamental differences between parametric and nonparametric test are discussed in the following points:. To make a choice between parametric and the nonparametric test is not easy for a researcher conducting statistical analysis. For performing hypothesis, if the information about the population is completely known, by way of parameters, then the test is said to be parametric test whereas, if there is no knowledge about population and it is needed to test the hypothesis on population, then the test conducted is considered as the nonparametric test.

Thank u guys for simplifying this for us…. Millions of thanks to all the readers of the page, for liking and sharing your valuable opinions with us, keep reading. This article is really helpful… Cheers to Surbhi S for creating this article and pls do continue on creating articles like this…. This material provides very good clarity on the parametric and non-parametric difference. Thank you so much for this article, especially the Hypothesis Test Hierarchy chart. I am reviewing statistics, and this chart serves as a roadmap.

Your email address will not be published. Save my name, email, and website in this browser for the next time I comment. Key Differences Between Parametric and Nonparametric Tests The fundamental differences between parametric and nonparametric test are discussed in the following points: A statistical test, in which specific assumptions are made about the population parameter is known as the parametric test.

A statistical test used in the case of non-metric independent variables is called nonparametric test. In the parametric test, the test statistic is based on distribution.

On the other hand, the test statistic is arbitrary in the case of the nonparametric test. In the parametric test, it is assumed that the measurement of variables of interest is done on interval or ratio level. As opposed to the nonparametric test, wherein the variable of interest are measured on nominal or ordinal scale.

In general, the measure of central tendency in the parametric test is mean, while in the case of the nonparametric test is median.

In the parametric test, there is complete information about the population. Conversely, in the nonparametric test, there is no information about the population. The applicability of parametric test is for variables only, whereas nonparametric test applies to both variables and attributes. Comments Very nice article.

The information is very detailed and easy to grab. This is excellent. The flowchart was really helpful. Thank you. We are really contented with your views, this means a lot, keep sharing. These informations are very helpful to understand the concepts.

Information is clear to understand, very helpful. This is super helpful! It is well detailed and easy to understand. This was extremely helpful on a very technical and difficult subject such as statistics. ## Parametric and Non-parametric tests for comparing two or more groups

In terms of selecting a statistical test, the most important question is "what is the main study hypothesis? For example, in a prevalence study there is no hypothesis to test, and the size of the study is determined by how accurately the investigator wants to determine the prevalence. If there is no hypothesis, then there is no statistical test. It is important to decide a priori which hypotheses are confirmatory that is, are testing some presupposed relationship , and which are exploratory are suggested by the data. No single study can support a whole series of hypotheses. A sensible plan is to limit severely the number of confirmatory hypotheses.

Need a hand? All the help you want just a few clicks away. Therefore, several conditions of validity must be met so that the result of a parametric test is reliable. They can thus be applied even if parametric conditions of validity are not met. Parametric tests often have nonparametric equivalents. ## Service Unavailable in EU region

Parametric statistics is a branch of statistics which assumes that sample data comes from a population that can be adequately modeled by a probability distribution that has a fixed set of parameters. Most well-known statistical methods are parametric. The normal family of distributions all have the same general shape and are parameterized by mean and standard deviation.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy. See our Privacy Policy and User Agreement for details. Published on Jun 15, Он замер, когда его взгляд упал на монитор. Как при замедленной съемке, он положил трубку на место и впился глазами в экран. За восемь месяцев работы в лаборатории Фил Чатрукьян никогда не видел цифр в графе отсчета часов на мониторе ТРАНСТЕКСТА что-либо иное, кроме двух нулей.

Он это отлично знает. Стратмор провел рукой по вспотевшему лбу. - Этот шифр есть продукт нового типа шифровального алгоритма, с таким нам еще не приходилось сталкиваться.

Сьюзан подбежала к. - Коммандер. Стратмор даже не пошевелился. - Коммандер. Нужно выключить ТРАНСТЕКСТ.

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