7 ms·

This was my thoughts too. The values appear to be fairly similar for all three, especially given the large variations. Not to mention that they're explicitly t

by ezzaf 9y ago

This was my thoughts too. The values appear to be fairly similar for all three, especially given the large variations. Not to mention that they're explicitly trialling this with people "for whom celiac disease had been excluded". So an alternate conclusion could be "people without celiac disease but with self reported gut issues don't respond significantly differently to fructan, gluten, and placebo". Which is... not surprising?

ameister14 9y ago
They don't appear that similar to me - if you plot them graphically you'll see a significant difference, particularly at the lower end.
- karlkatzke 9y ago
  P values of less than .005 generally mean that you accept the null hypothesis. The authors did otherwise for one hypothesis, which I find interesting.
  - alexandercrohde 9y ago
    You're wrong on two accounts. You want a low p-value, the lower the p-value the stronger the evidence. A p-value is cutoff traditionally at .05 (1 in 20), and if it's p < .05 you can reject the null hypothesis. The "null hypothesis" is the OPPOSITE of your paper's hypothesis (so you want to reject it): the null hypothesis is "All the variation we saw was from chance"
    - nonbel 9y ago
      Why not make the null hypothesis the same as your paper's hypothesis? Wouldn't that make more sense?
      - themadryaner 9y ago
        You cannot prove the null hypothesis; you can only disprove it. The p-value is the probability that the null hypothesis is true. So if the null hypothesis is that there is no difference, and there is a low probability of that being true, then you have shown that there is a difference between the groups. If the p-value is high, you do not show that the null hypothesis is true. Instead, you show that you did not find a statistically significant difference. This can happen when the difference between the values is small or there is not enough data to make the difference clear, which is why a high p-value is not enough to reject the alternative hypothesis.
        jostylr 9y ago
        > The p-value is the probability that the null hypothesis is true. No, the p-value is the probability that the given data generated is as far away or farther by random chance given the null hypothesis is true. That is to say, we assume the null hypothesis to make some predictions and see if the data is a likely occurrence under those assumptions. This is not the same as the probability that the null hypothesis is true. If that is what you want (and most of us do want this), then Bayesian methods are more appropriate though they are more complicated and more sensitive to initial assumptions.
        nonbel 9y ago
        >"You cannot prove the null hypothesis; you can only disprove it." Sure, but thats why the null hypothesis should be predicted by your theory. Then you are checking your theory. Most people could care less about whether there is a miniscule difference between groups or not. >"The p-value is the probability that the null hypothesis is true." No, as pointed out by jostylr this is wrong.
      - fnovd 9y ago
        The null hypothesis signifies a lack of relatedness. Give circumstance A and outcome B, we make no assumptions, i.e. we assume A and B are unrelated by not assuming they are related. This is the null hypothesis. In order to reject that, we must provide compelling evidence. If you start by assuming A and B are related, your experiment would have to fail reject that notion in order to prove that A and B are indeed related, which is a less direct way of approaching scientific discovery.

by ezzaf 9y ago

ameister14 9y ago

They don't appear that similar to me - if you plot them graphically you'll see a significant difference, particularly at the lower end.

karlkatzke 9y ago
P values of less than .005 generally mean that you accept the null hypothesis. The authors did otherwise for one hypothesis, which I find interesting.
- alexandercrohde 9y ago
  You're wrong on two accounts. You want a low p-value, the lower the p-value the stronger the evidence. A p-value is cutoff traditionally at .05 (1 in 20), and if it's p < .05 you can reject the null hypothesis. The "null hypothesis" is the OPPOSITE of your paper's hypothesis (so you want to reject it): the null hypothesis is "All the variation we saw was from chance"
  - nonbel 9y ago
    Why not make the null hypothesis the same as your paper's hypothesis? Wouldn't that make more sense?
    - themadryaner 9y ago
      You cannot prove the null hypothesis; you can only disprove it. The p-value is the probability that the null hypothesis is true. So if the null hypothesis is that there is no difference, and there is a low probability of that being true, then you have shown that there is a difference between the groups. If the p-value is high, you do not show that the null hypothesis is true. Instead, you show that you did not find a statistically significant difference. This can happen when the difference between the values is small or there is not enough data to make the difference clear, which is why a high p-value is not enough to reject the alternative hypothesis.
      - jostylr 9y ago
        > The p-value is the probability that the null hypothesis is true. No, the p-value is the probability that the given data generated is as far away or farther by random chance given the null hypothesis is true. That is to say, we assume the null hypothesis to make some predictions and see if the data is a likely occurrence under those assumptions. This is not the same as the probability that the null hypothesis is true. If that is what you want (and most of us do want this), then Bayesian methods are more appropriate though they are more complicated and more sensitive to initial assumptions.
      - nonbel 9y ago
        >"You cannot prove the null hypothesis; you can only disprove it." Sure, but thats why the null hypothesis should be predicted by your theory. Then you are checking your theory. Most people could care less about whether there is a miniscule difference between groups or not. >"The p-value is the probability that the null hypothesis is true." No, as pointed out by jostylr this is wrong.
    - fnovd 9y ago
      The null hypothesis signifies a lack of relatedness. Give circumstance A and outcome B, we make no assumptions, i.e. we assume A and B are unrelated by not assuming they are related. This is the null hypothesis. In order to reject that, we must provide compelling evidence. If you start by assuming A and B are related, your experiment would have to fail reject that notion in order to prove that A and B are indeed related, which is a less direct way of approaching scientific discovery.