Is Probability Truly Meaningful in Research Studies?

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In summary, the conversation discusses the concept of probability and the use of p-values in research. It is noted that the .05 level used in psychology is arbitrary and may not accurately reflect the significance of results. Additionally, the idea of comparing results to chance is questioned, as there are various interpretations of what is considered random. The concept of hypothesis testing is mentioned and the reliability of random number generators is also discussed. There is a debate about the meaning of "random" and "chance" and their application in statistics. Finally, the compilation and calculation of critical values is briefly mentioned as a sound mathematical technique.
  • #1
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A couple of points, I hope not too muddled:

I find probability difficult to understand, but I sometimes get concerned when a small p value is taken as proof-positive. I know that the .05 level used in psychology is arbitrary (though it probably makes sense) and Cohen points out that research in psychology has traditionally lacked statistical power, making nonsense of the .05 level when used in such studies.

When a researcher says that 'compared to chance' their results are significant, I'm starting to wonder what they mean. You hear things like '50 people score above chance on a card-guess study which is evidence for psi'; what exactly are we comparing the guesses to? I know if there are 5 cards in the deck, they have a 1 in 5 chance... or do they? I've heard that a random number generator may throw up non-random sequences, so how predictable is chance?
 
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  • #2
This looks familiar. Repost?

I would imagine that the research Cohen talks about might be the kind whereby things are modeled mathematically without any justification that the model is accurate, or that the samples are too small.

Anyway, the idea is this: we have a large population and a certain proportion of them share some trait (for example). We sample a small segement of them in some suitably `random' fashion (how one takes the sample is important), and then analyse it statistically, to estimate the proportion of the whole population with that trait. When we construct confidence intervals we are NOT saying that there is a 95% chance that the actual population proportion is in the range (for the 5% margin), but that if we repeat the sampling again and again, then in the long run 95% of the time we expect the statistic to lie in that region.


The last thing about random and non-random is a very dodgy area - because you're opening it up to all kinds of interpretations as to what you mean by random.

Firstly there is no such thing as a random number generator, really.

Secondly, a truly random set of digits, in the statistical sense, will contain things that human perception would deem as not random.

Example, you toss a coin 5 times and obtain HHHHH (ie 5 heads), is that more or less likely than obtaining HTHHT? Obviously the chances ought to be the same (in the model). but we think of one as being more likely, don't we?

The compared to chance thing is called hypothesis testing. It is a large area, you should google for it.
 
  • #3
matt grime said:
This looks familiar. Repost?

Yeah. I thought if I posted it as a new thread someone might answer - luckily you did.

matt grime said:
I would imagine that the research Cohen talks about might be the kind whereby things are modeled mathematically without any justification that the model is accurate, or that the samples are too small.

I'm picking up a classic paper of his sometime soon, so I'll post anything of interest.

matt grime said:
The last thing about random and non-random is a very dodgy area - because you're opening it up to all kinds of interpretations as to what you mean by random.

Firstly there is no such thing as a random number generator, really.

Secondly, a truly random set of digits, in the statistical sense, will contain things that human perception would deem as not random.

This is the bit that I find a bit mind-boggling. I think I might just be worried that within any random sequence of numbers, there might - by chance - be non-random sequences. Or perhaps like repeating patterns from chaos theory. So in these cases (or all cases?) scoring 'above chance' is meaningless, as 1/ chance is never the same in any two instances, except maybe when 2/ non-random patterns emerge, by chance. Would an immortal monkey typist eventually write the works of Shakespeare?

I might feel better if I knew how tables of critical values are compiled or calculated. On the other hand, I feel a bit over my head with this whole area, but somehow I want to understand it a little better.
 
  • #4
Ok, you;re using the words "random" and "chance" in some very odd ways. I think you ought to figure out whaty you mean by them.

Let's suppose that yuo have a string of digits and each digit is one of 0 or 1, and each digit is equally likely to be either 0 or 1. If there are 10 digits in the string what is the probability that the sub string 10 occurs? Suppose that we have a string of a bollion digits, what is that probability that 10 *doesn't* occur?

The works of shakespeare are just a finite string of 'digits'. If we took larger and larger strings in the same symbols, then with probability 1 that string will occur eventually - just as it's unlikely for 10 not to appear in a strnig of one billion digits it's unlikely that "the works of shakespeare" won't appear in a string of a googol of digits.


Critical values use soudn mathematical technique about the distributions of samples. If you're bothered look up the details (t-distributions, chi squared and so on).
 

FAQ: Is Probability Truly Meaningful in Research Studies?

What is chance and how is it related to predictability?

Chance refers to the likelihood or probability of a particular event occurring. It is related to predictability in that the more chance involved in a situation, the less predictable the outcome will be.

Can chance be calculated or measured?

Yes, chance can be calculated and measured using mathematical principles such as probability and statistics. These tools allow us to quantitatively understand and predict the likelihood of certain events occurring.

Is everything in life based on chance?

Not everything in life is based on chance. While some events may seem random or unpredictable, there are often underlying factors or patterns that can help us understand and predict outcomes.

How does chance play a role in scientific research?

Chance plays a significant role in scientific research, particularly in experiments and studies. Researchers use statistical methods to account for and analyze the role of chance in their results, and to determine if the findings are significant or due to chance.

Can chance be controlled or manipulated?

In some cases, chance can be controlled or manipulated through various factors such as probability, statistics, and other variables. However, there are also instances where chance cannot be controlled and must be accounted for in our understanding and predictions.

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