Reproducibility: Difference between revisions

Revision as of 17:43, 24 May 2025

Revision as of 11:28, 28 May 2025

Line 4:

'''Reproducibility''', closely related to '''replicability''' and '''repeatability''', is a major principle underpinning the [[scientific method]]. For the findings of a study to be reproducible means that results obtained by an [[experiment]] or an [[observational study]] or in a [[statistical analysis]] of a [[data set]] should be achieved again with a high degree of reliability when the study is replicated. There are different kinds of replication<ref>{{Cite journal|last1=Tsang|first1=Eric W. K.|last2=Kwan|first2=Kai-man|date=1999|title=Replication and Theory Development in Organizational Science: A Critical Realist Perspective|url=http://dx.doi.org/10.5465/amr.1999.2553252|journal=Academy of Management Review|volume=24|issue=4|pages=759–780|doi=10.5465/amr.1999.2553252|issn=0363-7425}}</ref> but typically replication studies involve different researchers using the same methodology. Only after one or several such successful replications should a result be recognized as scientific knowledge.

== Types of Reproducibilityreproducibility ==

There are different kinds of replication studies, each serving a unique role in scientific validation:

Direct Replicationreplication – The exact experiment or study is repeated under the same conditions to verify the original findings.

Conceptual Replicationreplication – A study tests the same hypothesis but uses a different methodology, materials, or population to see if the results hold in different contexts.

Computational Reproducibilityreproducibility – In data science and computational research, reproducibility requires making all datasets, code, and algorithms openly available so others can replicate the analysis and obtain the same results.

== Importance of Reproducibility ==