Answers.Body: <p>This is non-parametric statistics, the mean requires conformation to the normal distribution (but there are exceptions), or perhaps better put it requires a fixed relationship between mean and standard deviation. Non-parametric stats always use the median and are distribution free. Wilcoxon test/ Mann-Whitney U test  are used to replace t-test. The Kruskal-Wallis test replaces the 1-way ANOVA
  Finally the Friedman test replaces 2-way ANOVA but has its critiques.  The t- and ANOVA tests require normality, F-max is one approach to determine normality.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-01-01T00:51:14.067Z
Answers.Text: Thanks. upvoted for the response
Answers.UserDisplayName: The Great
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-03-13T12:34:02.970Z
Answers.Text: :-) thanks @subhash, likewise.
Answers.UserDisplayName: M__
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-12-31T14:48:21.540Z
Answers.LastActivityDate: 2024-02-09T15:31:29.483Z
Answers.LastEditDate: 2024-02-09T15:31:29.483Z
Answers.UserDisplayName: M__
Answers.Body: <p>Another viewpoint could be : statistically significant implies use of sampling theory to establish presence of significant difference  between  case and control groups. The "clinical " significance seems to test for correctness or validity of measures of variables. Apparenty, the measurement theory is meant for checking validity of data - the difference  could arise on account of incorrect measurements. The non-parameteric tests -  it seems - take care of measurement errors and hence, produce perhaps more reliable results.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-03-13T03:15:46.337Z
Answers.Text: Thanks upvoted for the response
Answers.UserDisplayName: The Great
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-03-12T12:52:22.393Z
Answers.LastActivityDate: 2020-03-12T12:52:22.393Z
Answers.UserDisplayName: Subhash C. Davar
Body: <p>I am working on a clinical data analysis project and am from IT domain.</p>
  
  <p>While I am analyzing the data, I came to know that though some of the variables are not statistically significant, they are clinically significant.</p>
  
  <p>I also read that difference in median values between case and control group for variables is used as a measure to indicate clinical significance</p>
  
  <p>Can someone help me understand this with a simple example as to why difference in median between groups is considered for clinical significance?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-06-10T08:08:32.387Z
Comments.Text: nice and a great question
Comments.UserDisplayName: Subhash C. Davar
Tags: <database><errors><validation><p-values>
Title: Difference between clinical significance and statistical significance
ThreadText: # Difference between clinical significance and statistical significance
  
  (Asked by: username_0 on 2019-12-29)
  
  I am working on a clinical data analysis project and am from IT domain.
  
  While I am analyzing the data, I came to know that though some of the variables are not statistically significant, they are clinically significant.
  
  I also read that difference in median values between case and control group for variables is used as a measure to indicate clinical significance
  
  Can someone help me understand this with a simple example as to why difference in median between groups is considered for clinical significance?
  
  **Tags:** database, errors, validation, p-values
  
  > **Comments:**
  > **username_1**: nice and a username_0 question  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2020-03-12. Score: 2 | Accepted answer
  
  Another viewpoint could be : statistically significant implies use of sampling theory to establish presence of significant difference between case and control groups. The "clinical " significance seems to test for correctness or validity of measures of variables. Apparenty, the measurement theory is meant for checking validity of data - the difference could arise on account of incorrect measurements. The non-parameteric tests - it seems - take care of measurement errors and hence, produce perhaps more reliable results.
  
  > **Comments:**
  > **username_0**: Thanks upvoted for the response  
  
  ---
  
  ### Answer by username_2 on 2019-12-31. Score: 3
  
  This is non-parametric statistics, the mean requires conformation to the normal distribution (but there are exceptions), or perhaps better put it requires a fixed relationship between mean and standard deviation. Non-parametric stats always use the median and are distribution free. Wilcoxon test/ Mann-Whitney U test are used to replace t-test. The Kruskal-Wallis test replaces the 1-way ANOVA
  Finally the Friedman test replaces 2-way ANOVA but has its critiques. The t- and ANOVA tests require normality, F-max is one approach to determine normality.
  
  > **Comments:**
  > **username_0**: Thanks. upvoted for the response  
  > **username_2**: :-) thanks @username_1, likewise.  
  
  ---
  
  