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3

$begingroup$

The title of the Comment in Nature Scientists rise up against statistical significance begins with:

Valentin Amrhein, Sander Greenland, Blake McShane and more than 800 signatories call for an end to hyped claims and the dismissal of possibly crucial effects.

and later contains statements like:

Again, we are not advocating a ban on P values, confidence intervals or other statistical measures — only that we should not treat them categorically. This includes dichotomization as statistically significant or not, as well as categorization based on other statistical measures such as Bayes factors.

I think I can grasp that the image below does not say that the two studies disagree because one "rules out" no effect while the other does not. But the article seems to go into much more depth that I can understand.

Towards the end there seems to be a summary in four points. Is it possible to summarize these in even simpler terms for those of us who read statistics rather than write it?

When talking about compatibility intervals, bear in mind four things.

• First, just because the interval gives the values most compatible with the data, given the assumptions, it doesn’t mean values outside it are incompatible; they are just less compatible...




• Second, not all values inside are equally compatible with the data, given the assumptions...




• Third, like the 0.05 threshold from which it came, the default 95% used to compute intervals is itself an arbitrary convention...




• Last, and most important of all, be humble: compatibility assessments hinge on the correctness of the statistical assumptions used to compute the interval...

---

statistical-significance p-value bias

share|cite|improve this question

asked 56 mins ago

uhohuhoh

1264

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add a comment | 

3

$begingroup$

The title of the Comment in Nature Scientists rise up against statistical significance begins with:

Valentin Amrhein, Sander Greenland, Blake McShane and more than 800 signatories call for an end to hyped claims and the dismissal of possibly crucial effects.

and later contains statements like:

Again, we are not advocating a ban on P values, confidence intervals or other statistical measures — only that we should not treat them categorically. This includes dichotomization as statistically significant or not, as well as categorization based on other statistical measures such as Bayes factors.

I think I can grasp that the image below does not say that the two studies disagree because one "rules out" no effect while the other does not. But the article seems to go into much more depth that I can understand.

Towards the end there seems to be a summary in four points. Is it possible to summarize these in even simpler terms for those of us who read statistics rather than write it?

When talking about compatibility intervals, bear in mind four things.

• First, just because the interval gives the values most compatible with the data, given the assumptions, it doesn’t mean values outside it are incompatible; they are just less compatible...




• Second, not all values inside are equally compatible with the data, given the assumptions...




• Third, like the 0.05 threshold from which it came, the default 95% used to compute intervals is itself an arbitrary convention...




• Last, and most important of all, be humble: compatibility assessments hinge on the correctness of the statistical assumptions used to compute the interval...

---

statistical-significance p-value bias

share|cite|improve this question

asked 56 mins ago

uhohuhoh

1264

$endgroup$

add a comment | 

$begingroup$

The title of the Comment in Nature Scientists rise up against statistical significance begins with:

Valentin Amrhein, Sander Greenland, Blake McShane and more than 800 signatories call for an end to hyped claims and the dismissal of possibly crucial effects.

and later contains statements like:

Again, we are not advocating a ban on P values, confidence intervals or other statistical measures — only that we should not treat them categorically. This includes dichotomization as statistically significant or not, as well as categorization based on other statistical measures such as Bayes factors.

I think I can grasp that the image below does not say that the two studies disagree because one "rules out" no effect while the other does not. But the article seems to go into much more depth that I can understand.

Towards the end there seems to be a summary in four points. Is it possible to summarize these in even simpler terms for those of us who read statistics rather than write it?

When talking about compatibility intervals, bear in mind four things.

• First, just because the interval gives the values most compatible with the data, given the assumptions, it doesn’t mean values outside it are incompatible; they are just less compatible...




• Second, not all values inside are equally compatible with the data, given the assumptions...




• Third, like the 0.05 threshold from which it came, the default 95% used to compute intervals is itself an arbitrary convention...




• Last, and most important of all, be humble: compatibility assessments hinge on the correctness of the statistical assumptions used to compute the interval...

---

statistical-significance p-value bias

share|cite|improve this question

asked 56 mins ago

uhohuhoh

1264

$endgroup$

share|cite|improve this question

asked 56 mins ago

uhohuhoh

1264

share|cite|improve this question

asked 56 mins ago

uhohuhoh

1264

asked 56 mins ago

uhohuhoh

1264

asked 56 mins ago

uhohuhoh

1264

1 Answer
1

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4

$begingroup$

I'll try.

1. The confidence interval (which they rename compatibility interval) shows the values of the parameter that are most compatible with the data. But that doesn't mean the values outside the interval are absolutely incompatible with the data.


2. Values near the middle of the confidence (compatibili5y) interval are more compatible with the data than values near the ends of the interval.


3. 95% is just a convention. You can compute 90% or 99% or any% intervals.


4. The confidence/compatibility intervals are only helpful if the experiment was done properly, if the analysis was done according to a preset plan, and the data conform with the assumption of the analysis methods. If you've got bad data analyzed badly, the compatibility interval is not meaningful or helpful.

share|cite|improve this answer

answered 43 mins ago

Harvey MotulskyHarvey Motulsky

10.9k44485

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add a comment | 

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4

$begingroup$

I'll try.

1. The confidence interval (which they rename compatibility interval) shows the values of the parameter that are most compatible with the data. But that doesn't mean the values outside the interval are absolutely incompatible with the data.


2. Values near the middle of the confidence (compatibili5y) interval are more compatible with the data than values near the ends of the interval.


3. 95% is just a convention. You can compute 90% or 99% or any% intervals.


4. The confidence/compatibility intervals are only helpful if the experiment was done properly, if the analysis was done according to a preset plan, and the data conform with the assumption of the analysis methods. If you've got bad data analyzed badly, the compatibility interval is not meaningful or helpful.

share|cite|improve this answer

answered 43 mins ago

Harvey MotulskyHarvey Motulsky

10.9k44485

$endgroup$

add a comment | 

4

$begingroup$

I'll try.

1. The confidence interval (which they rename compatibility interval) shows the values of the parameter that are most compatible with the data. But that doesn't mean the values outside the interval are absolutely incompatible with the data.


2. Values near the middle of the confidence (compatibili5y) interval are more compatible with the data than values near the ends of the interval.


3. 95% is just a convention. You can compute 90% or 99% or any% intervals.


4. The confidence/compatibility intervals are only helpful if the experiment was done properly, if the analysis was done according to a preset plan, and the data conform with the assumption of the analysis methods. If you've got bad data analyzed badly, the compatibility interval is not meaningful or helpful.

share|cite|improve this answer

answered 43 mins ago

Harvey MotulskyHarvey Motulsky

10.9k44485

$endgroup$

add a comment | 

$begingroup$

I'll try.

1. The confidence interval (which they rename compatibility interval) shows the values of the parameter that are most compatible with the data. But that doesn't mean the values outside the interval are absolutely incompatible with the data.


2. Values near the middle of the confidence (compatibili5y) interval are more compatible with the data than values near the ends of the interval.


3. 95% is just a convention. You can compute 90% or 99% or any% intervals.


4. The confidence/compatibility intervals are only helpful if the experiment was done properly, if the analysis was done according to a preset plan, and the data conform with the assumption of the analysis methods. If you've got bad data analyzed badly, the compatibility interval is not meaningful or helpful.

share|cite|improve this answer

answered 43 mins ago

Harvey MotulskyHarvey Motulsky

10.9k44485

$endgroup$

share|cite|improve this answer

answered 43 mins ago

Harvey MotulskyHarvey Motulsky

10.9k44485

answered 43 mins ago

Harvey MotulskyHarvey Motulsky

10.9k44485

answered 43 mins ago

Harvey MotulskyHarvey Motulsky

10.9k44485