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## What is non-parametric test?

Non-parametric tests are the statistical methods that are used to fit the data into the normal distribution. This would use the data that is not so reliant on numbers, but are reliant on ranking and orders. The perfect example for a non-parametric test is to conduct a survey by taking the preferences of the consumers, including their likes and dislikes, which are considered to be the actual data. The non parametric statistics have gained a huge prominence due to its ease of use. With the need of parameters is relieved, you can use the same data to carry out different tests. Statistics is very helpful to use for mean, the size of the sample, standard deviation or estimating a parameter when nothing is available. The non-parametric tests will either make a few or no assumptions about the datasets. There are many statistical tests that are coming up recently do not need to have assumptions on distribution of the population, nor do they need to have a hypothesis that is as per the parameter values specified. This is the test that is valid only for the distribution of the parent population. These tests are called as non-parametric or distribution free tests, since they do not rely on any of the population parameters like variance and mean. These are free for distribution as they do not rely on the shape of the distribution.

There are a few key concepts on in statistics that deal with non-parametric tests. There include – Signed rank test, Mann-Whitney U test, Kruskal Wallis test, etc. These concepts in statistics are less challenging for the students to understand. However, our talented pool of statisticians will help you to write the assignments on these topics with a great perfection. Our experts will write all sorts of assignment and for all the academic levels. Our tutors will be available round the clock to craft high quality and accurate assignments by taking away the burden from the shoulders of the students. No student needs to take the stress anymore by entrusting us.

Few of the non-parametric tests that are available include:

**Sign test**: This applies for both the univariate and bivariate data. There is a sample sign test that is used when you are picking the sample from the continuous symmetrical population. The probability of the sample value would be higher than that of the mean. The procedure that is embraced to carry out sign test would be simple and easy to conduct.

**Paired sign test**: There are two samples that are picked and compared to find the difference between the mean of the population. The difference that is observed between two paired observations from the picked samples will be helpful to carry out **hypothesis testing** . If you want to test the population in these randomly picked samples, then these should be distributed normally. If you want to test the difference between two means of two different populations, you need to make sure to have population variance as the same. You cannot apply any of these assumptions in any of the situations. When you have encountered this kind of scenario, you can use non-parametric test. The sign test would be based on different signs like plus, minus of the difference in the observed pairs without taking the magnitude of the differences into consideration.

**Median test:** This is the procedure that is followed to test two or multiple independent samples that are picked from the population that is having the same median. You cannot use the paired sign test when you pick two samples from two different populations. You can use the paired sign test only pre or post treatment and when the observations are in pairs. In median test, you can pick the samples from two different populations. This median test is most popular and is widely used to test whether or not the samples that you have picked independently belong to the population that has the same media. You can use the same kind of procedure for more than two different samples.

## Various topics on which our experts offer help

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**Anderson–Darling test:** This is a kind of statistical test that is used to check whether or not the sample data that is given is picked from the given probability distribution. Many students find it tough to write assignment on this topic and look for the help from experts. We have helped many students in writing the assignment on this topic flawlessly. You can avail our **Nonparametric Tests project Help** service and get the best writing aid.

**Cochran’s Q**: This is an extension to that of McNemar test and is a non-parametric test that is used to validate whether or not the K treatments would have the same effects. If you do not have enough time to write the assignment on this topic, you can order the assignment from us. We have a team of **Nonparametric Tests homework Help** experts who would work on the assignment and deliver it on time.

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**Kaplan–Meier**: This is also called as product limit estimator, which is a non-parametric statistic that helps you to easily estimate the survival function of the collected data for the lifetime. If you feel that writing assignment on this topic is nerve racking, then you can avail our** Nonparametric Tests homework Help**. We have experts who are well versed on this topic to offer you the best help.

## Non-parametric test assignment help

Non-parametric test would not assume anything when it is dealing with a sample or when the data is found to be quantitative. This is the critical method that is used when the data is not having a clear numerical interpretation. If you are struggling hard to write an assignment on any of the topics related to non-parametric test, you can take the help of our subject matter experts who hold ample knowledge backed by industry and academic experience. They write unique content that is free from plagiarism. The students do not need to take the pressure when are running out of the time given by the professors by entrusting us the responsibility of writing the assignment. You can use this valuable time to prepare for the exams.

Anderson–Darling test | Math Statistics Questions |

Cochran’s Q | Measures of Central tendency- Mean, Median ,Mode |

Cohen’s kappa | The spearman rank correlation process. |

Friedman two-way analysis of variance by ranks | Kendall’s W |

Kaplan–Meier | Kolmogorov–Smirnov test |

Kendall’s tau | Binary Predictors |

Multivariate | Common Misconceptions about Fit |

Median test | Logrank Test |

Pitman’s permutation test | Fitted Regression |

Rank products | Siegel–Tukey test |

Multi Co linearity | Spearman’s rank correlation coefficient |

Statistical Bootstrap Methods | Wald–Wolfowitz runs test |

The Kruskal-Wallis H or evaluation | The signal evaluation |

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