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And what should be used as an alternative? There is no reason to believe banning p value would result in improved research quality. Desperate researchers will j

And what should be used as an alternative? There is no reason to believe banning p value would result in improved research quality. Desperate researchers will just find another technique to game.

genericone 12y ago
My thinking is that there should be no alternative to NHSTP that would function as a crutch, that if something else were to arise in an attempt to take the place of NHSTP, it will not be gain as much prominence among researchers. Are there any other scalar statistical numbers in statistics that can function the same as p-values? That is: 1. Can the general public see it as jargon? The general public see p-value, and they think 'oh, those scientists probably know what that really means, just tell me if that number should be big or small to be meaningful' 2. Can a headline be made of it? ( Hypothesis X shown to be 'statistically significant' because a journalist saw the p-value result) 3. If a colleague does not understand it, can you make fun of them for not understanding such a 'simple' concept?
aliasbanana 12y ago
Yup. There is no way to resolve it. Everything besides numbers can be used to argue against them, because all numbers (optimistically) can measure is a very precise and pedantic simplification of an carefully constructed argument. In the case of hard sciences and engineering, it's direct sensory data that feeds the argument and maintains the rigor. Every person's eyes more or less, function the same, lest we descend into philosophizing about language, continuity of meaning and perception. Science will eventually have to come to terms with realizing that interpretation and truth is much more variant than the ideals of science reach for. A lot of social sciences have accepted this, and even teach this. But the expectation is people should then be more intelligent, hard working, and overall have greater competency than the norm demands. Unfortunately this is what creates part of the problem - the ability to bring a pattern into actual existence, by observing it before it actually exists. I don't like the idea of survival of the fittest in research and ideas though. I think it starts with simple questions, like 'what are we looking to prove/demonstrate/etc?', 'why/how?', which is concurrently processed with stuff like ethical and moral introspection and discussion. But the big point of science half the time is that no one knows what they are doing. The prestige and authority surrounding academia doesn't really place as much emphasis on this as it should.
beeworker 12y ago
http://en.wikipedia.org/wiki/Bayes_factor http://en.wikipedia.org/wiki/Bayes_factor
yummyfajitas 12y ago
Instead of blindly applying a tool you don't understand, you'll need to build a statistical model, explain why it's valid, and then construct a meaningful measurement based on it. Then the referee and reader will be required to understand it and will have the ability to critique it.
- disgruntledphd2 12y ago
  Which is good, as it will discourage people from submitting there, which means more potential papers for those of us who got on this train a while ago.

And what should be used as an alternative? There is no reason to believe banning p value would result in improved research quality. Desperate researchers will just find another technique to game.

genericone 12y ago

My thinking is that there should be no alternative to NHSTP that would function as a crutch, that if something else were to arise in an attempt to take the place of NHSTP, it will not be gain as much prominence among researchers. Are there any other scalar statistical numbers in statistics that can function the same as p-values? That is: 1. Can the general public see it as jargon? The general public see p-value, and they think 'oh, those scientists probably know what that really means, just tell me if that number should be big or small to be meaningful' 2. Can a headline be made of it? ( Hypothesis X shown to be 'statistically significant' because a journalist saw the p-value result) 3. If a colleague does not understand it, can you make fun of them for not understanding such a 'simple' concept?

aliasbanana 12y ago

Yup. There is no way to resolve it. Everything besides numbers can be used to argue against them, because all numbers (optimistically) can measure is a very precise and pedantic simplification of an carefully constructed argument. In the case of hard sciences and engineering, it's direct sensory data that feeds the argument and maintains the rigor. Every person's eyes more or less, function the same, lest we descend into philosophizing about language, continuity of meaning and perception. Science will eventually have to come to terms with realizing that interpretation and truth is much more variant than the ideals of science reach for. A lot of social sciences have accepted this, and even teach this. But the expectation is people should then be more intelligent, hard working, and overall have greater competency than the norm demands. Unfortunately this is what creates part of the problem - the ability to bring a pattern into actual existence, by observing it before it actually exists. I don't like the idea of survival of the fittest in research and ideas though. I think it starts with simple questions, like 'what are we looking to prove/demonstrate/etc?', 'why/how?', which is concurrently processed with stuff like ethical and moral introspection and discussion. But the big point of science half the time is that no one knows what they are doing. The prestige and authority surrounding academia doesn't really place as much emphasis on this as it should.

beeworker 12y ago

http://en.wikipedia.org/wiki/Bayes_factor http://en.wikipedia.org/wiki/Bayes_factor

yummyfajitas 12y ago

Instead of blindly applying a tool you don't understand, you'll need to build a statistical model, explain why it's valid, and then construct a meaningful measurement based on it. Then the referee and reader will be required to understand it and will have the ability to critique it.

disgruntledphd2 12y ago
Which is good, as it will discourage people from submitting there, which means more potential papers for those of us who got on this train a while ago.