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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
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
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

Quick Guide To Perform Hypothesis Testing

L
Likhitha Last Updated : 05 Dec, 2020
4 min read

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

Introduction

Hypothesis Testing

Hypothesis Testing is necessary for almost every sector, it does not limit to Statisticians or Data Scientists. For example, if we develop a code we perform testing too. In the same way, for every product or problem that an organization shows, it has to be solved by providing assumptions. This can be done using “Hypothesis Testing”.

Before going to that, let’s know what exactly Hypothesis means:

“Hypothesis is described as a recommended solution for an undefinable incident which doesn’t into current theory”.

The actual definition of Hypothesis Testing is by which an analyst tests an assumption regarding a population parameter. The methodology retained by the analyst depends on the nature of the data used and the reason for the analysis.

hypothesis testing project

Hypothesis Testing

Steps to perform Hypothesis Testing:

  1. Define null and alternative hypothesis

  2. Examine data, check assumptions

  3. Calculate Test Statistic

  4. Determine the Corresponding p-value

  5. Make a decision about the null hypothesis.

To perform all these steps, let us take an example to understand easily.

Problem: Considering Italian adults from the age group 18-30 living in Italy, Do males have significantly higher mean Body Mass Index (BMI) than females?

Here the population is Italian adults (18-30) in Italy and the parameter of interest is Body Mass Index (BMI)

Step-1: Define Hypotheses

  • Null: There is no difference in mean BMI
    H(0): U1=U2 [U1 represents the population mean BMI for Males and U2 represents the population mean BMI for females] Here H(0) says that they are equal to each other
  • Alternative: There is a significant difference in mean BMI
    H(A): U1=U2 [U1 represents the population mean BMI for Males and U2 represents the population mean BMI for females] Here H(A) says that they are not equal to each other
  • Significance level = 5%

 

Step-2: Examine data and checking assumptions

In this step, the data was filtered to include only Italian adults that were between the ages of 18 and 30. After this, we need to do some statistics calculations like mean, minimum, maximum, standard deviation, and sample sizes for both males and females.

Some of the assumptions that we need to check are as follows:

  • Samples are considered simple random samples
  • Samples are independent of one another
  • Both populations of responses are approximately normal or sample sizes are both large enough.

 

Step-3: Calculate Test Statistic:

Test Statistic is a measure of how far our sample statistic is from our hypothesized population parameter, in terms of estimated standard errors.

  • Z= Best Estimate – null value /  Estimated standard error
  • The Best Estimate is the difference between the male and female statistic sample mean
  • The null value is the hypothesized null value
  • The estimated standard error for two means can change depending on which approach we are going to use.
  • The two approaches that you can use are the pooled approach and the unpooled approach.
  • The pooled approach is the variance of two populations are assumed to be equal.
  • The unpooled approach is the assumption of equal variances is dropped.

 

Step-4: Determining P-value:

P-value is determined by assuming the null hypothesis is true, it is the probability of observing a test statistic of a value(Z) or more extreme.

So we are going to calculate this probability using Z-distribution where df = n1+n2-2

we need to check both sides since it is a two-sided alternative hypothesis because our alternative is not equal too. so, we have to check both the upper and lower tail of our distribution.

The distribution graph looks like given below with its corresponding sample size and the degrees of freedom:

 

Hypothesis testing - pvalue

Distribution curve

From the above graph, we can see both our positive test statistic value and below the negative test statistic value. This means that if the difference in population mean BMI between males and females was really zero, so if that null hypothesis was true, then observing a difference in sample means of the test statistic value or something more extreme is fairly likely. There is almost a 20 percent chance of seeing that because this value is so large, we are going to go ahead and fail to reject the null.

 

Step-5: Make a Decision

If P-value is larger than the significance level, which means there is weak evidence against the null. Thus we fail to reject the null hypothesis.

So, in summary, hypothesis tests are used to put theories about a parameter of interest to the test. Here, that parameter is the difference in population means. The basic steps for performing this hypothesis test. First, we’re going to define our hypotheses. Then, we’re going to examine our data while checking our assumptions and calculating our test statistic. With this test statistic, we’ll determine our corresponding p-value, and then finally, we will make a decision based on this value.

The assumptions for the two-sample t-test for population means are that we need both sets of data to be two simple random samples and they need to be independent of one another. We need to make sure that both populations of responses are normally distributed. If not, we need to make sure we at least have a large sample size so we can apply the central limit theorem. Whether or not our population variances are equal is also crucial in determining whether we use a pooled or unpooled approach. Finally, we need to know how to interpret the p-value, the decision, and our final conclusion. These are all very important when conducting a hypothesis test.

For more articles, do check out this profile:

https://likhithakakanuru.medium.com/

blogathonHypothesis Testing
L
Likhitha

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