Parametric vs Non-Parametric Tests: Advantages and Disadvantages by Aaron Zhu Geek Culture

Parametric vs Non-Parametric Tests: Advantages and Disadvantages by Aaron Zhu Geek Culture

Nonparametric tests are also less sensitive to outliers, which can have a significant impact on the results of parametric tests. Nonparametric tests and parametric tests are two types of statistical tests that are used to analyze data and make inferences about a population based on a sample. Nonparametric tests are used when the data do not follow a normal distribution or when the assumptions of parametric tests are not met.

Parametric tests are those that make assumptions about the parameters of the population distribution from which the sample is drawn. This is often the assumption that the population data are normally distributed. Non-parametric tests are “distribution-free” parametric vs nonparametric and, as such, can be used for non-Normal variables. Table 3 shows the non-parametric equivalent of a number of parametric tests. Statistical analysis plays a crucial role in understanding and interpreting data across various disciplines.

I think that the word “effective” in the accepted answer should be deleted. Because due to the different number of effective parameters, as Aksakal pointed out, the accepted answer implies that Ridge and Lasso are non-parametric, but it doesn’t seem to be true. Effective parameters (effective degrees of freedom) are characteristics of a learning algorithm, but not a model itself.

  1. If you want to know more about statistics, methodology, or research bias, make sure to check out some of our other articles with explanations and examples.
  2. B The Kruskal-Wallis test is used for comparing ordinal or non-Normal variables for more than two groups, and is a generalisation of the Mann-Whitney U test.
  3. If you already know what types of variables you’re dealing with, you can use the flowchart to choose the right statistical test for your data.

The next question is “what types of data are being measured?” The test used should be determined by the data. The choice of test for matched or paired data is described in Table 1 and for independent data in Table 2. A neural net with fixed architecture and no weight decay would be a parametric model. This is not disimilar to how the position and shape of graphs of quadratic functions of the following form depend only on the parameters of $a$, $h$, and $k$. It uses F-test to statistically test the equality of means and the relative variance between them.

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Exploring Continuous Variable

When the p-value falls below the chosen alpha value, then we say the result of the test is statistically significant. T-tests are used when comparing the means of precisely two groups (e.g., the average heights of men and women). ANOVA and MANOVA tests are used when comparing the means of more than two groups (e.g., the average heights of children, teenagers, and adults). Connect and share knowledge within a single location that is structured and easy to search. This website is using a security service to protect itself from online attacks.

Nonparametric statistics, therefore, fall into a category of statistics sometimes referred to as distribution-free. Often nonparametric methods will be used when the population data has an unknown distribution, or when the sample size is small. Nonparametric tests are particularly useful when the sample size is small or the data are skewed or ordinal, as they are more forgiving of deviations from the assumptions of parametric tests.

Basics of Ensemble Techniques

In the case of the non-parametric test, the test is based on the differences in the median. If the independent variables are non-metric, the non-parametric test is usually performed. One of the main drawbacks is that they are generally less powerful than parametric tests, which means that they may not be able to detect small but significant differences between groups. Nonparametric tests also tend to have less precise estimates of the population parameters and may not provide as much information about the relationships between variables as parametric tests. Parametric methods are statistical techniques that rely on specific assumptions about the underlying distribution of the population being studied.


Peter Westfall is a distinguished professor of information systems and quantitative sciences at Texas Tech University. He specializes in using statistics in investing, technical analysis, and trading. A. The 4 non-parametric tests are wilcoxon signed-rank test, mann-Whitney U test, kruskal-Wallis test and spearman correlation coefficient. This test is used for comparing two or more independent samples of equal or different sample sizes. Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

Neural networks are inspired by the workings of the human brain, and they can be used to solve a wide variety of problems, including regression and classification tasks. Linear regression — Linear regression is used to predict the value of a target variable based on a set of input variables. It is often used for predictive modeling tasks, such as predicting the sales volume of a product based on historical sales data. Familiar clinical examples include blood pressure, ejection fraction, forced expiratory volume in 1 second (FEV1), serum cholesterol, and anthropometric measurements.

Differences Between Parametric and Non-parametric Test

PCA would be parametric, because the equations are well defined, but CCA can be nonparametric, because we are looking for correlations across all variables, and if these are Spearman’s correlations, we have a nonparametric model. I think clustering algorithms would be nonparametric, unless we are looking for clusters of certain shape. Originally I thought “parametric vs non-parametric” means if we have distribution assumptions on the model (similar to parametric or non-parametric hypothesis testing). But both of the resources claim “parametric vs non-parametric” can be determined by if number of parameters in the model is depending on number of rows in the data matrix.

If you want to know more about statistics, methodology, or research bias, make sure to check out some of our other articles with explanations and examples. However, a non-parametric test (sometimes referred to as a distribution free test) does not assume anything about the underlying distribution (for example, that the data comes from a normal (parametric distribution). In Statistics, a parametric test is a kind of hypothesis test which gives generalizations for generating records regarding the mean of the primary/original population. The t-test is carried out based on the students’ t-statistic, which is often used in that value. Where n1 is the sample size for sample 1, and R1 is the sum of ranks in Sample 1. It is also known as the “Goodness of fit test” which determines whether a particular distribution fits the observed data or not.

Parametric tests usually have stricter requirements than nonparametric tests, and are able to make stronger inferences from the data. They can only be conducted with data that adheres to the common assumptions of statistical tests. Rank methods can generate strong views, with some people preferring them for all analyses and others believing that they have no place in statistics. We believe that rank methods are sometimes useful, but parametric methods are generally preferable as they provide estimates and confidence intervals and generalise to more complex analyses. Non-parametric methods are statistical techniques that do not rely on specific assumptions about the underlying distribution of the population being studied. These methods are often referred to as “distribution-free” methods because they make no assumptions about the shape of the distribution.

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