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Product and Process Comparisons 7. For a single process, the current state of the process can be compared with a nominal or hypothesized state. This section outlines techniques for answering the following questions from data gathered from a single process: Do the observations come from a particular distribution?

## Introduction

Non-parametric tests, as their name tells us, are statistical tests without parameters. They are also referred to as distribution-free tests due to the fact that they are based n fewer assumptions e. These tests are particularly used for testing hypothesis, whose data is usually non normal and resists transformation of any kind. Due to the lesser amount of assumptions needed, these tests are relatively easier to perform. They are also more robust.

The potential source of complexity while analyzing the data is to choose on whether the data collected could be analyzed properly by the application of parametric tests or nonparametric tests. This concern cannot be underrated as there are certain assumptions which should be fulfilled before analyzing the data by applying either of the two types of tests. This article describes in detail the difference between parametric and nonparametric tests, when to apply which and the advantages of using one over the other. Users Online: Rosner B. Fundamentals of Biostatistics.

## Nonparametric statistics

Nonparametric statistics is the branch of statistics that is not based solely on parametrized families of probability distributions common examples of parameters are the mean and variance. Nonparametric statistics is based on either being distribution-free or having a specified distribution but with the distribution's parameters unspecified. Nonparametric statistics includes both descriptive statistics and statistical inference. Nonparametric tests are often used when the assumptions of parametric tests are violated. The term "nonparametric statistics" has been imprecisely defined in the following two ways, among others. Order statistics , which are based on the ranks of observations, is one example of such statistics.

## What Are the Advantages and Disadvantages of the Parametric Test of Significance in Statistics?

The present review introduces nonparametric methods. Three of the more common nonparametric methods are described in detail, and the advantages and disadvantages of nonparametric versus parametric methods in general are discussed. Many statistical methods require assumptions to be made about the format of the data to be analysed. For example, the paired t-test introduced in Statistics review 5 requires that the distribution of the differences be approximately Normal, while the unpaired t-test requires an assumption of Normality to hold separately for both sets of observations.

Are you confused about whether you should pick a parametric test or go for the non-parametric ones? Usually, to make a good decision , we have to check the advantages and disadvantages of nonparametric tests and parametric tests. In this article, we are going to talk to you about parametric tests, parametric methods, advantages and disadvantages of parametric tests and what you can choose instead of them.

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.

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Three of the more common nonparametric methods are described in detail, and the advantages and disadvantages of nonparametric versus.