What does “Scientists rise up against statistical significance” mean? (Comment in Nature)References containing arguments against null hypothesis significance testing?Statistical significance for correlated significance testsDoes Greenhouse-Geisser correction influence the effect size estimation, the statistical significance treshold, or both of them?Is the “hybrid” between Fisher and Neyman-Pearson approaches to statistical testing really an “incoherent mishmash”?A psychology journal banned p-values and confidence intervals; is it indeed wise to stop using them?Statistical significanceStatistical test significanceASA discusses limitations of $p$-values - what are the alternatives?Is this the solution to the p-value problem?Statistical significance between years; what test to use
What is the smallest number n> 5 so that 5 ^ n ends with "3125"?
Air travel with refrigerated insulin
Why the various definitions of the thin space ,?
Did I make a mistake by ccing email to boss to others?
Sigmoid with a slope but no asymptotes?
The Digit Triangles
Telemetry for feature health
Can you identify this lizard-like creature I observed in the UK?
What happens if I try to grapple an illusory duplicate from the Mirror Image spell?
How to make a list of partial sums using forEach
How to preserve electronics (computers, iPads and phones) for hundreds of years
Review your own paper in Mathematics
Are Captain Marvel's powers affected by Thanos breaking the Tesseract and claiming the stone?
Isometric embedding of a genus g surface
Mimic lecturing on blackboard, facing audience
Has the laser at Magurele, Romania reached a tenth of the Sun's power?
Why is the Sun approximated as a black body at ~ 5800 K?
Limit max CPU usage SQL SERVER with WSRM
I'm just a whisper. Who am I?
Grepping string, but include all non-blank lines following each grep match
Why the "ls" command is showing the permissions of files in a FAT32 partition?
If Captain Marvel (MCU) were to have a child with a human male, would the child be human or Kree?
Can I run 125kHz RF circuit on a breadboard?
Is there a reason to prefer HFS+ over APFS for disk images in High Sierra and/or Mojave?
What does “Scientists rise up against statistical significance” mean? (Comment in Nature)
References containing arguments against null hypothesis significance testing?Statistical significance for correlated significance testsDoes Greenhouse-Geisser correction influence the effect size estimation, the statistical significance treshold, or both of them?Is the “hybrid” between Fisher and Neyman-Pearson approaches to statistical testing really an “incoherent mishmash”?A psychology journal banned p-values and confidence intervals; is it indeed wise to stop using them?Statistical significanceStatistical test significanceASA discusses limitations of $p$-values - what are the alternatives?Is this the solution to the p-value problem?Statistical significance between years; what test to use
$begingroup$
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
asked 1 hour 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
asked 1 hour 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
asked 1 hour ago
uhohuhoh
1264
$endgroup$
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
statistical-significance p-value bias
asked 1 hour ago
uhohuhoh
1264
asked 1 hour ago
uhohuhoh
1264
asked 1 hour ago
uhohuhoh
1264
asked 1 hour ago
uhohuhoh
1264
asked 1 hour ago
uhohuhoh
1264
1264
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
I'll try.
- 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.
- Values near the middle of the confidence (compatibili5y) interval are more compatible with the data than values near the ends of the interval.
- 95% is just a convention. You can compute 90% or 99% or any% intervals.
- 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.
answered 47 mins ago
Harvey MotulskyHarvey Motulsky
10.9k44485
$endgroup$
add a comment |
Your Answer
StackExchange.ifUsing("editor", function ()
return StackExchange.using("mathjaxEditing", function ()
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix)
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
);
);
, "mathjax-editing");
StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "65"
;
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function()
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled)
StackExchange.using("snippets", function()
createEditor();
);
else
createEditor();
);
function createEditor()
StackExchange.prepareEditor(
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader:
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
,
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
);
);
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstats.stackexchange.com%2fquestions%2f398646%2fwhat-does-scientists-rise-up-against-statistical-significance-mean-comment-i%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
I'll try.
- 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.
- Values near the middle of the confidence (compatibili5y) interval are more compatible with the data than values near the ends of the interval.
- 95% is just a convention. You can compute 90% or 99% or any% intervals.
- 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.
answered 47 mins ago
Harvey MotulskyHarvey Motulsky
10.9k44485
$endgroup$
add a comment |
$begingroup$
I'll try.
- 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.
- Values near the middle of the confidence (compatibili5y) interval are more compatible with the data than values near the ends of the interval.
- 95% is just a convention. You can compute 90% or 99% or any% intervals.
- 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.
answered 47 mins ago
Harvey MotulskyHarvey Motulsky
10.9k44485
$endgroup$
add a comment |
$begingroup$
I'll try.
- 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.
- Values near the middle of the confidence (compatibili5y) interval are more compatible with the data than values near the ends of the interval.
- 95% is just a convention. You can compute 90% or 99% or any% intervals.
- 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.
answered 47 mins ago
Harvey MotulskyHarvey Motulsky
10.9k44485
$endgroup$
I'll try.
- 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.
- Values near the middle of the confidence (compatibili5y) interval are more compatible with the data than values near the ends of the interval.
- 95% is just a convention. You can compute 90% or 99% or any% intervals.
- 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.
answered 47 mins ago
Harvey MotulskyHarvey Motulsky
10.9k44485
answered 47 mins ago
Harvey MotulskyHarvey Motulsky
10.9k44485
answered 47 mins ago
Harvey MotulskyHarvey Motulsky
10.9k44485
answered 47 mins ago
Harvey MotulskyHarvey Motulsky
10.9k44485
10.9k44485
add a comment |
add a comment |
Thanks for contributing an answer to Cross Validated!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstats.stackexchange.com%2fquestions%2f398646%2fwhat-does-scientists-rise-up-against-statistical-significance-mean-comment-i%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
