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== Welcome to Sustainability Methods! ==
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== Welcome to the ''Sustainability Methods Wiki!'' ==
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<br>
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The aim of this Wiki is to present and explain fundamental methods, terms and tools relevant to (Sustainability) Science and answer underlying questions. The Wiki is composed of several sub-wikis:
  
=== Day 1 - Intro ===
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{{ContentGrid
# [[Why statistics matters|Do models and statistics matter?]] Why does it pay to be literate in statistics and R?
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# Getting concepts clear: Generalisation, Sample, and Bias
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#:- See also, [[Misunderstood concepts in statistics|misunderstood concepts]]
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# [[Why statistics matters#A very short history of statistics|History of statistics]]
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=== Day 2 - Data formats based on R ===
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{{InfoCard
# [[Data formats#Continuous data|Continuous vs. categorical, and subsets]]
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|heading = [[Courses]] | class = center |
# [[Data distribution|Normal distribution]]
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# Poisson, binomial, Pareto
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This sub-wiki contains all material that supplements '''university courses'''.
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}}
  
=== Day 3 - Simple tests ===
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{{InfoCard
# [[Simple Tests|Parametric and non-parametric]]
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|heading = [[Methods]] | class = center |
# [[Hypothesis building|Hypothesis testing]]
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# [[Designing studies#P-value|The power of probability]]
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'''Methods''' are at the heart of scientific research. Learn about the most important methods in this sub-wiki.
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}}
  
=== Day 4 - Correlation and regression ===
 
# [[Correlations and regressions|Correlations_and_regressions]]
 
# [[Causality vs. correlation|Causal vs non-causal relations]]
 
#:- See also, [[Misunderstood concepts in statistics#Correlation|misunderstood concepts]]
 
# Are all correlations causal?
 
# Is the world linear?
 
# Transformation
 
  
=== Day 5 - Correlation and regression ===
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# [[Designing studies#P-value|P values]] vs. [[Designing studies#Sample size|sample size]]
 
# Residuals
 
# Reading correlation plots
 
  
=== Day 6 - Designing studies Pt. 1 ===
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{{ContentGrid
# How do I compare more than two groups?
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# Designing experiments - degrees of freedom
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# One way and two way
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=== Day 7 - Designing studies Pt. 2 ===
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{{InfoCard
# Balanced vs. unbalanced - Welcome to the Jungle
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|heading = [[Skills & Tools]] | class = center |
# [[Designing studies#Block effects|Block effects]]
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# Interaction and reduction
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Every type of work can be facilitated through appropriate '''Skills & Tools.''' Dive in and learn something new!
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}}
  
=== Day 8 - Types of experiments ===
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{{InfoCard
# Are all laboratory experiment really made in labs?
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|heading = [[Normativity of Methods]] | class = center |
# Are all field experiment really made in fields?
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# What are natural experiments?
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Learn more about issues on the philosophy of science and the question '''how methods relate to the 'real world''''.
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}}
  
=== Day 9 - Statistics from the Faculty ===
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}}
  
=== Day 10 - Statistics down the road ===
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== First time visitor? ==
# Multivariate Statistics
 
# AIC
 
  
=== Day 11 - The big recap ===
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* If this is your first visit, you may want to have a look at the [[Sustainability_Methods:About|About]] page. Here, you will find an introduction to what the Wiki is all about.
# Distribution & simple test
 
# Correlation and regression
 
#:- See also, [[Misunderstood concepts in statistics|misunderstood concepts]]
 
# [[Analysis of Variance]]
 
  
=== Day 12 - Models ===
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* In the [[FAQ]], you may find answers to some general questions concerning the Wiki. You can also use the [[Special:Contact|Contact form]] in case you would like to provide feedback or ask further questions.
# Are models wrong?
 
# Are models causal?
 
# Are models useful?
 
  
=== Day 13 - Ethics and norms of statistics ===
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* Now you can get started! Pick a sub-wiki above and choose one or more entries to learn about. <br/>
# What is informed consent?
 
# How does a board of ethics work?
 
# How long do you store data?
 
  
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* You can also generate a random entry on top of the page or read one of the 5 newest wiki entries that have been created on this wiki:
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{{Special:NewestPages/-/5}}
  
View [https://sustainabilitymethods.org/index.php/Special:AllPages All Pages].
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__NOTOC__
 
 
== Admin Tools ==
 
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:Configuration_settings Configuration settings list]
 
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:FAQ MediaWiki FAQ]
 
* [https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]
 
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Localisation#Translation_resources Localise MediaWiki for your language]
 
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:Combating_spam Learn how to combat spam on your wiki]
 

Revision as of 10:03, 30 September 2020

Welcome to the Sustainability Methods Wiki!


The aim of this Wiki is to present and explain fundamental methods, terms and tools relevant to (Sustainability) Science and answer underlying questions. The Wiki is composed of several sub-wikis:

Courses

This sub-wiki contains all material that supplements university courses.

Methods

Methods are at the heart of scientific research. Learn about the most important methods in this sub-wiki.

Skills & Tools

Every type of work can be facilitated through appropriate Skills & Tools. Dive in and learn something new!

Normativity of Methods

Learn more about issues on the philosophy of science and the question how methods relate to the 'real world'.

First time visitor?

  • If this is your first visit, you may want to have a look at the About page. Here, you will find an introduction to what the Wiki is all about.
  • In the FAQ, you may find answers to some general questions concerning the Wiki. You can also use the Contact form in case you would like to provide feedback or ask further questions.
  • Now you can get started! Pick a sub-wiki above and choose one or more entries to learn about.
  • You can also generate a random entry on top of the page or read one of the 5 newest wiki entries that have been created on this wiki:
  1. Linear Regression in Python
  2. How to write unreadable und unmaintainable code
  3. Handling Categorical Data in Python
  4. Factor Analysis
  5. Decision Trees in Python