An introduction to statistics, significance testing and the P value

Chu, Kevin (1999) An introduction to statistics, significance testing and the P value. Emergency Medicine Australasia, 11 1: 28-34.

Author Chu, Kevin
Title An introduction to statistics, significance testing and the P value
Journal name Emergency Medicine Australasia   Check publisher's open access policy
ISSN 1035-6851
Publication date 1999-03-01
Sub-type Article (original research)
Volume 11
Issue 1
Start page 28
End page 34
Total pages 7
Place of publication Richmond, Vic., Australia
Publisher Wiley-Blackwell
Language eng
Abstract P values come from statistical tests used for testing the null hypothesis (Ho) which states that there is no difference between treatment outcomes. If the P value is less than 0.05 there is a less than 5% probability that the difference is due to chance if the Ho is true. Given this low chance, the Ho is rejected and the alternate hypothesis (Ha) that there is a difference between treatment outcomes is accepted. In hypothesis testing, there is a risk of wrongly rejecting a true Ho (α error) and wrongly not rejecting a false Ho (β error). The t-and Chi-squared tests are the two most commonly used statistical tests for clinical hypothesis testing. Statistical significance derived from such tests, needs to be clinically significant for the finding to be relevant. P values need to be interpreted with the knowledge of several statistical concepts including sample size, multiple comparisons and the validity of the study.
Keyword Clinical statistics
Hypothesis testing
P value
Significance testing
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Medicine Publications
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Created: Thu, 03 Mar 2011, 01:35:32 EST by Dr Kevin Chu on behalf of Royal Brisbane Clinical School