Data Science
28
Hypothesis Testing
Home
1
Introduction
2
Schedule
R Programming
3
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4
Introduction to R
5
Getting Started
6
IDEs
7
R packages
8
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9
Data Types & Vectors
10
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11
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12
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13
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14
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15
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18
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19
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20
Function Scoping
21
The Apply Family
22
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Data Science
23
Terminal
24
Version Control with Git and GitHub
25
Demo Dataset
26
Probability
27
Statistics
28
Hypothesis Testing
29
Data Visualization
30
Introduction to Machine Learning
31
Clustering
32
Decomposition and Dimensionality Reduction
33
Supervised Learning
34
AI Assistants for Data Science
35
References
Table of contents
28.1
Learning objectives
28.2
From research question to hypotheses
28.3
Test statistics and p-values
28.4
Errors and power
28.5
Choosing a test and checking assumptions
28.6
Effect sizes and confidence intervals
28.7
Multiple comparisons
28.8
Worked example
28.9
Hands-on practice
28.10
Checkpoint
28.11
Further study
Data Science
28
Hypothesis Testing
28
Hypothesis Testing
28.1
Learning objectives
Translate a research question into null and alternative hypotheses.
Choose a test appropriate to the variables and study design.
Interpret a test alongside effect size and uncertainty.
28.2
From research question to hypotheses
28.3
Test statistics and p-values
28.4
Errors and power
28.5
Choosing a test and checking assumptions
28.6
Effect sizes and confidence intervals
28.7
Multiple comparisons
28.8
Worked example
28.9
Hands-on practice
28.10
Checkpoint
28.11
Further study
27
Statistics
29
Data Visualization