As usual in some disciplines, a Researcher conducted an experiment without having previously designed. The experiment consisted of exposing 4 different groups of mice at two types of food (simultaneously measuring and amount ingested preference) and each group was expusto turn to different conditions.
What would be the appropriate statistical procedure?
The problem I have is the dependence on preference.
As you are well aware, any analysis made on relationships seen or hypothesis made a posteriori requires the most conservative statistical analysis to exclude investigator bias. So if you are going to use a 2-way analysis of variance that Mark Perry has proposed for repeated measurements over time and within groups (and I agree), it behooves you to use the most conservative post hoc analysis available such as the Bonferroni method (J M Bland, D G Altman. Multiple significance tests: the Bonferroni method. BMJ 1995; 310(6973):170).
I have a bias that I need to express. As statistical packages evolved and continue to evolve, there has been in the past a knee-jerk response to quote the name of this package (SAS, SPSS, etc.) within the method section of their paper. Understand this, it is far more important to state the statistical method used then how you calculated it (on your computer, a hand-held calculator, an abacus, or your fingers and toes). Just a suggestion from a reviewer who tried to hammer this concept home for years.
In the field of pharmaceutical trials, were`i work, cross over designs are user when de same subject received the two different treatments after an washout period. Is that you case ?
As you are well aware, any analysis made on relationships seen or hypothesis made a posteriori requires the most conservative statistical analysis to exclude investigator bias. So if you are going to use a 2-way analysis of variance that Mark Perry has proposed for repeated measurements over time and within groups (and I agree), it behooves you to use the most conservative post hoc analysis available such as the Bonferroni method (J M Bland, D G Altman. Multiple significance tests: the Bonferroni method. BMJ 1995; 310(6973):170).
I have a bias that I need to express. As statistical packages evolved and continue to evolve, there has been in the past a knee-jerk response to quote the name of this package (SAS, SPSS, etc.) within the method section of their paper. Understand this, it is far more important to state the statistical method used then how you calculated it (on your computer, a hand-held calculator, an abacus, or your fingers and toes). Just a suggestion from a reviewer who tried to hammer this concept home for years.
Let me restate that. It is far more important to state the statistical method used AND WHY then how you calculated it.
@Norman
very grateful for your comment.
the problem most frequent occurrence is the application of statistical procedures to not having considered an initial design. especially in the area of biology and agriculture.
once again, some require a magician!
Some of the magic that statisticians pull out of their hat (or bag of tricks) are complex and sometimes convoluted (at least to my mind) especially in the world of nonparametric analysis. As far as initial design versus a posteriori observation (and redesign), don't feel alone. If you asked many researchers, you'll find a good portion of them who will tell you that they formulate their hypothesis as they sit down to write their manuscript -- if they have one at all. (The rest won't admit to it). That way they chose their statistical testing after the fact to fit their observed results. If you review enough manuscripts you will agree with what I am saying. That being said, I strongly believe that the most conservative statistical analysis should always be performed to be rigorous whether results were anticipated a priori or observed a posteriori.
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