<p><p>In simplest terms, they are searching for objective, observable truth. Wikipedia seems to have quite a few references to this, see the quote/link below. 
</p><p>In terms of the One-Factor-at-a-time, OFAT method was first recorded to have been used by James Lind.<br>
According to <a href="https://en.wikipedia.org/wiki/History_of_statistics#Design_of_experiments" rel="nofollow">Wikipedia</a>:<br>
<i>"In 1747, while serving as surgeon on HM Bark Salisbury, James Lind carried out a controlled experiment to develop a cure for scurvy. In this study his subjects' cases "were as similar as I could have them", that is he provided strict entry requirements to reduce extraneous variation. The men were paired, which provided blocking. From a modern perspective, the main thing that is missing is randomized allocation of subjects to treatments.</i>
</p></p>

<p>
Fast forward a hundred years or so, and:<br> 
<i>"As is well know, statistic methods--and by "statistical methods" I mean probability-based modeling and inference--entered into psychology in 1860 in the work of Gustav Fechner (1860) in psychophysics (Boring 1961;Stigler 1986)."</i>
 <a href="http://www.jstor.org/stable/1085417" rel="nofollow">
<br>A Historical View of Statistical Concepts in Psychology and Educational Research</a> <br><i>American Journal of Education, 101, 1992.</i></p>

CC BY-SA 3.0
2014-08-31T12:54:24.053Z
Hello Cbaker510, thanks for your answer. However it does not address my question: Why group statistics (instead of e.g. single subject examinations), why population average (instead of other distributional properties). The Stigler pointer seems to be correct though, I justed picked up a copy of his book on *The History of Statistics before 1900*, which includes a discussion of how psychology and social sciences were not only fields of application of statistics but also involved in the development of the field.
A. Donda
CC BY-SA 3.0
2014-08-31T18:56:56.327Z
Try looking at page 66-67 of the AJE article.
Cbaker510
CC BY-SA 3.0
2014-09-01T13:17:44.987Z
Yes, it seems like Quetelet has introduced the idea of an "average man" – not into psychology though but into the social sciences; apparently opposed by the founder of sociology, Comte. I'm currently reading up in Stigler's monograph if and how this line traces from Quetelet into psychology.
A. Donda
CC BY-SA 3.0
2014-08-30T04:08:14.540Z
2014-08-30T04:08:14.540Z
Cbaker510
<p>In experimental psychology and cognitive neuroscience today, with the exception of a few specific subdisciplines, the consensus seems to be that an experimental study has to be performed on a group of subjects which are considered as a sample from an underlying population. Statistics is then applied in such a way as to allow an inference on <em>properties of the distribution in that population</em> of a certain psychological function or feature, most often the population mean.</p>

<p>My question is: When was this consensus established, with which reasoning, and on whose initiative?</p>

<p>In my experience, current textbooks on statistics in psychology discuss the statistical procedures in great detail, but contain only a few cursory remarks on the <em>psychological</em> (not statistical) motivation of these procedures. They do not discuss why a cognitive scientist should be interested in a population-average reaction time, a population-average event-related potential etc. – instead of e.g. effects that are present in every single individual in the population, or a specific sub-population. Or maybe I'm just looking at the wrong books?</p>

CC BY-SA 3.0
2014-08-26T13:28:10.217Z
I think I can provide an answer, but I'm trying to understand what the difference is between investigating a population-average response time and 'effects that are present in every single individual in the population'
Josh
CC BY-SA 3.0
2014-08-26T16:41:00.990Z
@Josh: I think the difference is between effects that vary across individuals (hence are distributed in ways that make characteristics such as a mean and *SD* useful) and effects that do not (i.e., are *invariant* and universal / human nature), or at most vary nominally (i.e., differ in ways that can be described with discrete, unordered categories; e.g., sex, or maybe species).
Nick Stauner
CC BY-SA 3.0
2014-08-26T18:52:58.933Z
@Josh, assuming a symmetrical distribution, if the population average is larger in one condition than in another condition, this merely means that the response is larger in more than 50% of the individuals, and smaller in less than 50% of the individuals. If the effect was present in every single individual, this would mean that the response is larger in 100% of the individuals.
A. Donda
CC BY-SA 3.0
2014-08-26T18:53:51.123Z
But the question is not about this specific alternative goal, since many different alternatives are conceivable, but just who decided when that it is specifically the population mean that we are interested in. If you can provide an answer, please do so.
A. Donda
CC BY-SA 3.0
2014-08-26T18:58:34.263Z
@NickStauner, yes, but even if we have to assume that no such invariants exist, that still doesn't necessarily mean that "population mean differs" is the thing we should be looking for. Distributions can be characterized in many different ways.
A. Donda
CC BY-SA 3.0
2014-08-26T19:18:31.017Z
Preaching to the choir :) but a lot of the (false) consensus probably built up around traditional practices that were "established" when both psychology and statistics were young sciences, borrowing heavily from biological research methods I'm sure. This isn't meant to be an answer, but I worry that a good one will be elusive. So much of statistical practice is a matter of finding a useful approximation of the phenomenon of interest; even basic improvements in these models like choosing to focus on the median instead of the mean seem to occur gradually and without consensus.
Nick Stauner
<methodology><statistics>
Origin of group-level statistics in psychology and cognitive neuroscience
A. Donda
# Origin of group-level statistics in psychology and cognitive neuroscience

(Asked by: username_0 on 2014-08-26)

In experimental psychology and cognitive neuroscience today, with the exception of a few specific subdisciplines, the consensus seems to be that an experimental study has to be performed on a group of subjects which are considered as a sample from an underlying population. Statistics is then applied in such a way as to allow an inference on *properties of the distribution in that population* of a certain psychological function or feature, most often the population mean.

My question is: When was this consensus established, with which reasoning, and on whose initiative?

In my experience, current textbooks on statistics in psychology discuss the statistical procedures in great detail, but contain only a few cursory remarks on the *psychological* (not statistical) motivation of these procedures. They do not discuss why a cognitive scientist should be interested in a population-average reaction time, a population-average event-related potential etc. – instead of e.g. effects that are present in every single individual in the population, or a specific sub-population. Or maybe I'm just looking at the wrong books?

**Tags:** methodology, statistics

> **Comments:**
> **username_1**: I think I can provide an answer, but I'm trying to understand what the difference is between investigating a population-average response time and 'effects that are present in every single individual in the population'  
> **username_2**: @username_1: I think the difference is between effects that vary across individuals (hence are distributed in ways that make characteristics such as a mean and *SD* useful) and effects that do not (i.e., are *invariant* and universal / human nature), or at most vary nominally (i.e., differ in ways that can be described with discrete, unordered categories; e.g., sex, or maybe species).  
> **username_0**: @username_1, assuming a symmetrical distribution, if the population average is larger in one condition than in another condition, this merely means that the response is larger in more than 50% of the individuals, and smaller in less than 50% of the individuals. If the effect was present in every single individual, this would mean that the response is larger in 100% of the individuals.  
> **username_0**: But the question is not about this specific alternative goal, since many different alternatives are conceivable, but just who decided when that it is specifically the population mean that we are interested in. If you can provide an answer, please do so.  
> **username_0**: @NickStauner, yes, but even if we have to assume that no such invariants exist, that still doesn't necessarily mean that "population mean differs" is the thing we should be looking for. Distributions can be characterized in many different ways.  
> **username_2**: Preaching to the choir :) but a lot of the (false) consensus probably built up around traditional practices that were "established" when both psychology and statistics were young sciences, borrowing heavily from biological research methods I'm sure. This isn't meant to be an answer, but I worry that a good one will be elusive. So much of statistical practice is a matter of finding a useful approximation of the phenomenon of interest; even basic improvements in these models like choosing to focus on the median instead of the mean seem to occur gradually and without consensus.  

---

## 1 answers

### Answer by username_3 on 2014-08-30. Score: 1

In simplest terms, they are searching for objective, observable truth. Wikipedia seems to have quite a few references to this, see the quote/link below.

In terms of the One-Factor-at-a-time, OFAT method was first recorded to have been used by James Lind.  
According to [Wikipedia](https://en.wikipedia.org/wiki/History_of_statistics#Design_of_experiments):  
*"In 1747, while serving as surgeon on HM Bark Salisbury, James Lind carried out a controlled experiment to develop a cure for scurvy. In this study his subjects' cases "were as similar as I could have them", that is he provided strict entry requirements to reduce extraneous variation. The men were paired, which provided blocking. From a modern perspective, the main thing that is missing is randomized allocation of subjects to treatments.*

Fast forward a hundred years or so, and:  
*"As is well know, statistic methods--and by "statistical methods" I mean probability-based modeling and inference--entered into psychology in 1860 in the work of Gustav Fechner (1860) in psychophysics (Boring 1961;Stigler 1986)."*
[A Historical View of Statistical Concepts in Psychology and Educational Research](http://www.jstor.org/stable/1085417)   
*American Journal of Education, 101, 1992.*

> **Comments:**
> **username_0**: Hello username_3, thanks for your answer. However it does not address my question: Why group statistics (instead of e.g. single subject examinations), why population average (instead of other distributional properties). The Stigler pointer seems to be correct though, I justed picked up a copy of his book on *The History of Statistics before 1900*, which includes a discussion of how psychology and social sciences were not only fields of application of statistics but also involved in the development of the field.  
> **username_3**: Try looking at page 66-67 of the AJE article.  
> **username_0**: Yes, it seems like Quetelet has introduced the idea of an "average man" – not into psychology though but into the social sciences; apparently opposed by the founder of sociology, Comte. I'm currently reading up in Stigler's monograph if and how this line traces from Quetelet into psychology.  

---

