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  • Non-parametric Testing Assignment Help

Non-parametric Testing Assignment Help

A non-parametric test is also known as a distribution-free test. It does not make any assumptions regarding the underlying distribution, unlike a parametric test that makes assumptions on the parameters of a population such as mean and standard deviation. In statistics, the word non-parametric means that the target population does not have a normal distribution. A good example is with the one-way ANOVA, where one of the assumptions made is that data comes from a normal distribution. An analyst whose data is not normally distributed can use a non-parametric alternative – the Kruskal-Walis Test to conduct the experiment.

However, our statistics assignment tutors say that it is advisable to use parametric tests because they have greater statistical power and tend to be more accurate. This means that you will more likely find a true significant effect. A non-parametric test should only be used if the researcher knows that the normality assumption is being violated. Moreover, you should also note that non-parametric tests perform better with non-normal continuous data. Avail of our non-parametric testing assignment help for instant support with your homework. Our statistics experts are just a click of the mouse away, ready to help you submit impressive solutions for your assignment.

When to use non-parametric tests

As mentioned above, parametric tests are never used in the best possible ways when the data is not normal. Therefore, it is essential to find out if you have normally distributed data or not. You can figure this out even if you don’t have a graph. Simply check the Kurtosis and Skewness of the distribution using the Excel software. If you are not familiar with this, the non-parametric testing homework help professionals at Statistics Assignment Experts can assist you with this.

A normal distribution is symmetrical in shape and centered. In other words, it has no skewness. On the other hand, Kurtosis is how much of the data is in the tails of the center. Learn more about all this by getting professional statistics assignment help from us. We have experts who possess extensive knowledge about all the complicated concepts of statistics.

If your skewness and Kurtosis deviate a lot, consider using a non-parametric test such as the Chi-square test, or you risk getting meaningless results.

Other reasons to perform non-parametric tests

One or more parametric test assumptions have been violated
The sample size is too small, and you cannot run a parametric test
You cannot remove the outliers on your data
You have a very skewed distribution, and you want to test for the median instead of mean

Types of non-parametric tests

The chi-square test may be the only non-parametric test you came across while studying elementary statistics. You should know that there are several others like the Mann-Whitney and the Kruskal-Willis, which are alternatives for two-sample t-test and one-way ANOVA, respectively. Our college statistics homework help service caters to all non-parametric tests such as:

  • 1-sample sign test
This test is mostly used to estimate the median of a population and comparing it to a target or reference value.

  • 1-sample Wilcoxon signed-rank test
One-sample Wilcoxon signed-rank test is a popular test that is an alternative for the one-sample t-test. It is used when data cannot be assumed to be normally distributed. This test determines whether the known standard value is equal to the estimated median. However, there should be roughly the same number of values below and above the median. In other words, the data should be distributed symmetrically.

  • Friedman Test
It is the non-parametric alternative to the one-way ANOVA. The Friedman test checks for differences between groups for an ordinal dependent variable.

  • Goodman Kruskal’s Gamma
It is a measure of rank correlation. Goodman Kruskal’s Gamma measures the direction and strength of the association that exist between two variables measured on an ordinal scale.

  • Kruskal-Wallis test
The Kruskal Wallis test is also an alternative for one-way ANOVA. It is used to find out if two or more medians are different.

  • The Mann-Kendall Trend Test
This type of test is used to look for trends in time-series data. Commonly known as the M-K test, it is used to analyze consistent increase or decrease in trends in Y values for data collected over time.

  • Mann-Whitney Test
The Mann-Whitney test is used to compare the differences between two independent groups. The dependent variables of these groups should either be ordinal or continuous.

  • Mood’s Median Test
The Mood's median test is an alternative for the sign test. It is used when the researcher has two independent samples.

  • Spearman Rank Correlation
It is used to determine the correlation between two sets of data.

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Advantages of non-parametric tests

Non-parametric tests have a plethora of benefits over parametric tests. Some of these include;

They have more statistical power, especially when assumptions for parametric tests have been violated
Non-parametric tests have fewer assumptions
They accept small size samples
Non-parametric tests support all data types, including interval variables, normal variables, and data that has been measured imprecisely or have outliers.

Disadvantages of non-parametric tests

Some of the notable shortcomings of these tests are:

Less favorable for tests where parametric assumptions have not been violated
The analyst has to perform some calculations by hand. They are more labor-intensive
Many computer software packages do not have critical value tables for many non-parametric tests.

You should not hesitate to get in touch with the experts at Statisticsassignmentexperts.com when you need first-class assignment help with any of the following topics:

  • Measures of central tendency, mode, median and mean
  • Pitman’s permutation tests
  • Math statistics questions
  • Logrank test
  • Kuiper’s test
  • Kendall’s tau
  • Kaplan-Meier
  • Cohen’s Kappa
  • Anderson-Darling test
  • Cochran’s Q
  • Statistical Bootstrap Methods
  • Kolmogorov- Smirnov test
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