https://github.com/sivkri/hints-for-statistics-and-machine-learning-models

This file will give you an overall idea to choose appropriate statistical test
https://github.com/sivkri/hints-for-statistics-and-machine-learning-models

statistical-tests statistics

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This file will give you an overall idea to choose appropriate statistical test

• Host: GitHub
• URL: https://github.com/sivkri/hints-for-statistics-and-machine-learning-models
• Owner: sivkri
• Created: 2021-11-05T12:57:38.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2023-04-21T11:51:27.000Z (about 3 years ago)
• Last Synced: 2025-06-06T03:05:30.030Z (about 1 year ago)
• Topics: statistical-tests, statistics
• Homepage:
• Size: 953 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
which statistical test to be used

# Data Type

_Categorical (Qualitative) String_

1. Nominal (Gender) aka Factor/ Genotype  [MAIN EFFECT]

   

   levels - WT, Mutant

   

2. Ordinal (size )

_Numerical (Quantitative) Float/ Integer_

1. Continuous (Decimal) aka Age

2. Discrete (Whole)

# Choose a test based on below parameters

1. Aim

2. Parameter

3. No.of Groups

4. Study Design

5. Distribution

6. Type of Analysis

# Machine Learning

1. Supervised Learning (labeled training data)- Class or Label (Classification)

2. Unsupervised Learning (hidden structure from "unlabeled" data)- Numeric or Continuous (Clustering / Regression)

## Prediction

The output generated by a machine learning models for a particuolar problem 

There are majorly two kinds of predictions corresponding to two types of problem:

1. Classification (the prediction is mostly a class or label, to which a data points belong)

2. Regression (the prediction is a number, a continous a numeric value, because regression problems deal with predicting the value)