Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Parametric and Non-Parametric Tests: The Complete Guide

Aashi
Aashi Last Updated : 10 Oct, 2024
8 min read

Hypothesis testing is a cornerstone of statistics, vital for statisticians, machine learning engineers, and data scientists. It involves using statistical tests to determine whether to reject the null hypothesis, which assumes no relationship or difference between groups. These tests, whether parametric or non-parametric, are essential for analyzing data sets, handling outliers, and understanding p-values and statistical power. This article explores various statistical tests, including parametric tests like T-test and Z-test, and non-parametric tests, which do not assume a specific data distribution. Through these tests, we can draw meaningful conclusions from our data.Also,in this article we explain about the parametric and non parametric test , and types of parametric and Non Parametric test etc.

Learning Outcomes

  • Differentiate between parametric analysis and non-parametric methods, understanding their applications in data analysis.
  • Apply regression techniques to analyze relationships between variables in data science.
  • Conduct parametric analysis on both small and large sample sizes, ensuring accurate interpretations.
  • Utilize non-parametric tests such as the Wilcoxon Signed Rank Test, Spearman correlation, and Chi-Square for data sets with ordinal data and non-normally distributed data.
  • Analyze blood pressure data and other health metrics using appropriate statistical methods.
  • Evaluate the differences in independent groups using both parametric and non-parametric methods.
  • Understand the significance of the distribution of the data in choosing the right statistical test.
  • Integrate statistical tests into broader data science projects for robust analysis and insights.

This article was published as a part of the Data Science Blogathon.

Table of contents

What is a Parametric Test?

The basic principle behind the parametric tests is that we have a fixed set of parameters that are used to determine a probabilistic model that may be used in Machine Learning as well.

Parametric tests are those tests for which we have prior knowledge of the population distribution (i.e, normal), or if not then we can easily approximate it to a normal distribution which is possible with the help of the Central Limit Theorem.

Parameters for using the normal distribution is:

  • Mean
  • Standard Deviation

Why Do We Need a Parametric Test?

Eventually, the classification of a test to be parametric is completely dependent on the population assumptions. There are many parametric tests available from which some of them are as follows:

  • To find the confidence interval for the population means with the help of known standard deviation.
  • To determine the confidence interval for population means along with the unknown standard deviation.
  • To find the confidence interval for the population variance.
  • To find the confidence interval for the difference of two means, with an unknown value of standard deviation.

What is a Non-parametric Test?

In Non-Parametric tests, we don’t make any assumption about the parameters for the given population or the population we are studying. In fact, these tests don’t depend on the population.
Hence, there is no fixed set of parameters is available, and also there is no distribution (normal distribution, etc.) of any kind is available for use.

Why Do we Need Non-parametric Test?

This is also the reason that non-parametric tests are also referred to as distribution-free tests.
In modern days, Non-parametric tests are gaining popularity and an impact of influence some reasons behind this fame is –

  • The main reason is that there is no need to be mannered while using parametric tests.
  • The second reason is that we do not require to make assumptions about the population given (or taken) on which we are doing the analysis.
  • Most of the nonparametric tests available are very easy to apply and to understand also i.e. the complexity is very low.

Differences Between Parametric and Non-parametric Test

ParameterParametric TestNonparametric Test
AssumptionsAssume normal distribution and equal varianceNo assumptions about distribution or variance
Data TypesSuitable for continuous dataSuitable for both continuous and categorical data
Test StatisticsBased on population parametersBased on ranks or frequencies
PowerGenerally more powerful when assumptions are metMore robust to violations of assumptions
Sample SizeRequires larger sample size, especially when distributions are non-normalRequires smaller sample size
Interpretation of ResultsStraightforward interpretation of resultsResults are based on ranks or frequencies and may require additional interpretation
hypothesis testing, parametric test and Non - Parametric test | types of parametric test

Types of Parametric Tests for Hypothesis Testing

Let us explore types of parametric tests for hypothesis testing.

T-Test

  • It is a parametric test of hypothesis testing based on Student’s T distribution.
  • It is essentially, testing the significance of the difference of the mean values when the sample size is small (i.e, less than 30) and when the population standard deviation is not available.

Assumptions of this test:

  • Population distribution is normal, and
  • Samples are random and independent
  • The sample size is small.
  • Population standard deviation is not known.

Mann-Whitney ‘U’ test is a non-parametric counterpart of the T-test.

A T-test can be a:

One Sample T-test: To compare a sample mean with that of the population mean.

Introduction to Statistics for Uncertainty Analysis | isobudgets | hypothesis testing

where,

  • x̄ is the sample mean
  • s is the sample standard deviation
  • n is the sample size
  • μ is the population mean

Two-Sample T-test: To compare the means of two different samples.

Worksheet for how to calculate T Test | hypothesis testing

where,

  • x̄1 is the sample mean of the first group
  • x̄2 is the sample mean of the second group
  • S1 is the sample-1 standard deviation
  • S2 is the sample-2 standard deviation
  • n is the sample size

Note:

  • If the value of the test statistic is greater than the table value -> Rejects the null hypothesis.
  • If the value of the test statistic is less than the table value -> Do not reject the null hypothesis.

Z-Test

  • It is a parametric test of hypothesis testing.
  • It is used to determine whether the means are different when we know the population variance and the sample size is large (i.e., greater than 30).

Assumptions of this test:

  • Population distribution is normal
  • Samples are random and independent.
  • The sample size is large.
  • Population standard deviation is known.

A Z-test can be:

One Sample Z-test: To compare a sample mean with that of the population mean.

Z and T-tests from Scratch | types of parametric test

Two Sample Z-test: To compare the means of two different samples.

statmagic: two sample test of mean | hypothesis testing

where,

  • x̄1 is the sample mean of 1st group
  • x̄2 is the sample mean of 2nd group
  • σ1 is the population-1 standard deviation
  • σ2 is the population-2 standard deviation
  • n is the sample size

F-Test

  • It is a parametric test of hypothesis testing based on Snedecor F-distribution.
  • It is a test for the null hypothesis that two normal populations have the same variance.
  • An F-test regards a comparison of equality of sample variances.

F-statistic is simply a ratio of two variances.

F = s12/s22

Data Analysis in the Geosciences hypothesis testing | types of parametric test

By changing the variance in the ratio, F-test has become a very flexible test. It can then be used to:

  • Test the overall significance for a regression model.
  • To compare the fits of different models and
  • To test the equality of means.

Assumptions of this test:

  • Population distribution is normal, and
  • Researchers draw samples randomly and independently.

ANOVA 

  • Also called as Analysis of variance, it is a parametric test of hypothesis testing.
  • It is an extension of the T-Test and Z-test.
  • It tests the significance of the differences in the mean values among more than two sample groups.
  • It uses F-test to statistically test the equality of means and the relative variance between them.

Assumptions of this test:

  • Population distribution is normal, and
  • Samples are random and independent.
  • Homogeneity of sample variance.

One-way ANOVA and Two-way ANOVA are is types.

F-statistic = variance between the sample means/variance within the sample

Learn more about the difference between Z-test and T-test

Types of Non-parametric Tests

Let us now explore types of non-parametric tests.

Chi-Square Test

  • It is a non-parametric test of hypothesis testing.
  • It helps in assessing the goodness of fit between a set of observed and those expected theoretically.
  • It makes a comparison between the expected frequencies and the observed frequencies.
  • Greater the difference, the greater is the value of chi-square.
  • If there is no difference between the expected and observed frequencies, then the value of chi-square is equal to zero. It is also known as the “Goodness of fit test” which determines whether a particular distribution fits the observed data or not.

As a non-parametric test, chi-square can be used:

  •  test of goodness of fit.
  • as a test of independence of two variables.

Chi-square is also used to test the independence of two variables.

Conditions for chi-square test:

  • Randomly collect and record the Observations.
  • In the sample, all the entities must be independent.
  • No one of the groups should contain very few items, say less than 10.
  • The reasonably large overall number of items. Normally, it should be at least 50, however small the number of groups may be.

Chi-square as a parametric test is used as a test for population variance based on sample variance. If we take each one of a collection of sample variances, divide them by the known population variance and multiply these quotients by (n-1), where n means the number of items in the sample, we get the values of chi-square.

It is calculated as:

Chi-square test | types of parametric test | Microbe Notes, parametric and non-parametric test

Mann-Whitney U-Test

  • It is a non-parametric test of hypothesis testing.
  • This test examines whether two independent samples come from a population with the same distribution.
  • It acts as a true non-parametric counterpart to the T-test and offers the most accurate significance estimates, especially with small sample sizes and non-normally distributed populations.
  • It is based on the comparison of every observation in the first sample with every observation in the other sample.
  • The test statistic used here is “U”.
  • Maximum value of “U” is ‘n1*n2‘ and the minimum value is zero.

It is also known as:

  • Mann-Whitney Wilcoxon Test.
  • Mann-Whitney Wilcoxon Rank Test.

Mathematically, U is given by:

U1 = R1 – n1(n1+1)/2

where n1 is the sample size for sample 1, and R1 is the sum of ranks in Sample 1.

U2 = R2 – n2(n2+1)/2

When you consult the significance tables, use the smaller values of U1 and U2. The sum of the two values is given by,

U1 + U2 = { R1 – n1(n1+1)/2 } +  { R2 – n2(n2+1)/2 } 

Knowing that R1+R2 = N(N+1)/2 and N=n1+n2, and doing some algebra, we find that the sum is:

U1 + U2 = n1*n2

Kruskal-Wallis H-test

  • It is a non-parametric test of hypothesis testing.
  • Researchers use this test to compare two or more independent samples of equal or different sample sizes.
  • It extends the Mann-Whitney-U-Test, which is used to compare only two groups.
  • One-Way ANOVA is the parametric equivalent of this test. And that’s why it is also known as ‘One-Way ANOVA on ranks.
  • It uses ranks instead of actual data.
  • It does not assume the population to be normally distributed.
  • The test statistic used here is “H”.

Also Read: The Evolution and Future of Data Science Innovation

Conclusion

Understanding the distinctions and applications of parametric and non-parametric methods is crucial in quantitative data analysis. The choice between these methods depends on factors such as sample size, data distribution, and the presence of outliers. Techniques like the permutation test and the sign test provide robust alternatives when traditional assumptions are not met. Knowledge of standard deviation and other statistical measures enhances the reliability of your findings. For further reading and deeper insights into these topics, consult reputable sources such as Wiley publications.

Hope you like the article and get understanding of parametric and non-parametric tests it will help you for the get better understanding and also about the all about the parametric test.

Frequently Asked Questions

Q1. Is chi-square a non-parametric test?

Chi-square is a non-parametric test for analyzing categorical data, often used to see if two variables are related or if observed data matches expectations.

Q2. What are the 4 parametric tests? 

A. The 4 parametric tests are t-test, ANOVA (Analysis of Variance), pearson correlation coefficient
and linear regression.

Q3. What are the 4 non-parametric tests?

A. The four non-parametric tests include the Wilcoxon signed-rank test, Mann-Whitney U test, Kruskal-Wallis test, and Spearman correlation coefficient.

Q4. What is an example of a parametric test?

A. An example is the t-test, a parametric test that compares the means of two groups, assuming a normal distribution. Types include independent samples t-test, paired samples t-test, and one-sample t-test.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

blogathondata sciencedifference between parametric and non parametric testHypothesis Testingparametric and non parametric testParametric testparametric test and non parametric testparametric tests in statisticsstatisticstypes of parametric testwhat are parametric testswhat is parametric test
Aashi
Aashi

Currently, I am pursuing my Bachelor of Technology (B.Tech) in Electronics and Communication Engineering from Guru Jambheshwar University(GJU), Hisar. I am very enthusiastic about Statistics, Machine Learning and Deep Learning.

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

Ossai Joseph
Ossai Joseph

Thanks for the wonderful lecture.

123

Dr Vinayak kamlakar Raje
Dr Vinayak kamlakar Raje

I liked your article Can you provide atleast one example of each parametric test and non parametric test to understand application of each statistical tools

123

Ana Carol
Ana Carol

Great article, Aashi Goyal! Thank you for providing a comprehensive overview of parametric and non-parametric tests in statistics. The importance of understanding these tests cannot be overstated, as they play a crucial role in hypothesis testing. Your article effectively explains the key differences between the two types of tests, highlighting the assumptions, data types, and test statistics involved. It's a valuable resource for statisticians, data scientists, and machine learning engineers. Keep up the excellent work!

123

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag

Flash Card

What is hypothesis testing and why is it important in statistics?

Hey there! So, let's chat about hypothesis testing. It's like the backbone of statistics, super important for folks like statisticians, engineers, and data scientists. Basically, it's a way to figure out if there's a real difference or relationship between groups by using some fancy statistical tests. You start with something called the null hypothesis, which is like saying, \"Nah, there's no difference here.\" The cool part about hypothesis testing is that it gives us a structured way to make guesses about big groups of people or things based on a smaller sample. Here's why it's a big deal:

  • It helps us check if our assumptions are on point.
  • We can make smart decisions based on the data we have.
  • It lets us measure how strong the evidence is against the \"no difference\" idea.
Plus, it helps cut down on mistakes when we're making decisions by giving us a way to see how likely it is that we'd see our data if the null hypothesis were true. This is super handy for making sure our conclusions are solid and trustworthy.

What is hypothesis testing and why is it important in statistics?

Quiz

Hypothesis testing is a statistical method used to determine the validity of assumptions about population parameters based on sample data.

Flash Card

How do parametric tests differ from non-parametric tests in terms of assumptions and data requirements?

Alright, let's break down parametric vs. non-parametric tests. They're kinda like two different approaches to solving a problem. Parametric tests, like the T-test and Z-test, are used when we know or can guess that our data follows a normal distribution (think bell curve). They assume:

  • The data is normally distributed.
  • The groups have equal variance.
These tests are great for continuous data and pack a punch when those assumptions are met, but they do need a bigger sample size to work their magic. On the flip side, non-parametric tests, like the Wilcoxon Signed Rank Test and Chi-Square, don't care about the data's distribution. They're perfect for data that's more like rankings or doesn't fit the normal mold. Here's what makes them stand out:
  • They don't depend on population parameters.
  • They can handle both continuous and categorical data.
  • They work well with smaller sample sizes.
This makes non-parametric tests super flexible and a lifesaver when your data doesn't play by the parametric rules.

Quiz

Parametric tests assume a normal distribution and equal variance, while non-parametric tests do not rely on any specific data distribution.

Flash Card

Why might a researcher choose a non-parametric test over a parametric test?

So, why would someone pick a non-parametric test? Well, there are a few good reasons:

  • Data Distribution: Non-parametric tests don't need the data to be normally distributed, so they're great for skewed data or data with outliers.
  • Data Type: They can handle rankings and categories, while parametric tests usually stick to continuous data.
  • Sample Size: They need fewer data points, which is awesome if gathering data is tough or pricey.
  • Robustness: They're more forgiving if the data doesn't meet the strict assumptions of parametric tests.
So, if your data's a bit wild or you're working with a small sample, non-parametric tests are your go-to.

Quiz

Non-parametric tests are preferred when data are non-normally distributed, involve ordinal data, or when sample sizes are small.

Flash Card

What are the key differences in power and sample size requirements between parametric and non-parametric tests?

Let's talk power and sample size. Here's the scoop:

  • Power: Parametric tests are usually more powerful, meaning they're better at spotting real effects, but only if their assumptions are met. If not, their power takes a hit.
  • Sample Size: They need more data, especially if the data isn't normally distributed, to make sure the assumptions hold up.
  • Non-parametric Tests: They're not as powerful but are more forgiving with assumption violations. They can work with smaller samples and still give reliable results.
Choosing between these tests is a bit of a balancing act between power and how well they handle assumption hiccups, all depending on your data's quirks.

Quiz

Parametric tests are more powerful when assumptions are met and require larger sample sizes, while non-parametric tests are less powerful but require smaller samples.

Flash Card

How does the Central Limit Theorem facilitate the use of parametric tests?

Ah, the Central Limit Theorem (CLT) – it's like a magic trick for statisticians! It says that if you take enough samples, the average of those samples will look like a normal distribution, no matter what the original data looks like. Here's why that's awesome:

  • It lets us use parametric tests like the T-test and Z-test even if we don't know the population distribution, as long as the sample size is big enough.
  • It helps us assume normality of the data, which is a big deal for parametric tests.
Thanks to the CLT, we can use parametric tests on a wider range of data, making them more versatile and powerful – especially when figuring out the population distribution is tricky.

Quiz

The Central Limit Theorem states that the sampling distribution of the sample mean approaches normality as sample size increases, aiding parametric test use.

Flash Card

What factors should be considered when choosing between parametric and non-parametric methods?

When you're picking between parametric and non-parametric methods, here's what to keep in mind:

  • Data Distribution: If your data's normally distributed, go parametric. If not, non-parametric is the way to go.
  • Sample Size: Bigger samples are great for parametric tests, while smaller ones work better with non-parametric tests.
  • Data Type: Continuous data fits parametric tests, but ordinal or categorical data needs non-parametric tests.
  • Presence of Outliers: Non-parametric tests handle outliers better, so they're a safer bet if you've got some wild data points.
Considering these factors helps you pick the right method for your data, ensuring your analysis is spot-on and trustworthy.

Quiz

Data distribution, sample size, data type, and presence of outliers are key factors in choosing between parametric and non-parametric methods.

Congratulations, You Did It!
Well Done on Completing Your Learning Journey. Stay curious and keep exploring!
Parametric and Non-Parametric Tests: The Complete Guide
Parametric and Non-Parametric Tests: The Complete Guide
Comprehensive Guide on Non Parametric Tests
Comprehensive Guide on Non Parametric Tests

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details