Sfdwhf

Identifying "long and narrow" polygons in with PostGIS

How to I force windows to use a specific version of SQLCMD?

How much do grades matter for a future academia position?

What is the meaning of "You've never met a graph you didn't like?"

Language involving irrational number is not a CFL

In One Punch Man, is King actually weak?

Can I say "fingers" when referring to toes?

Is there anyway, I can have two passwords for my wi-fi

Alignment of six matrices

Can I run 125kHz RF circuit on a breadboard?

What is the smallest number n> 5 so that 5 ^ n ends with "3125"?

Difference between shutdown options

Anime with legendary swords made from talismans and a man who could change them with a shattered body

How to get directions in deep space?

How can I, as DM, avoid the Conga Line of Death occurring when implementing some form of flanking rule?

Should I assume I have passed probation?

The Digit Triangles

Can I cause damage to electrical appliances by unplugging them when they are turned on?

How to make money from a browser who sees 5 seconds into the future of any web page?

Why do Radio Buttons not fill the entire outer circle?

Can you identify this lizard-like creature I observed in the UK?

What does "tick" mean in this sentence?

Does the Crossbow Expert feat's extra crossbow attack work with the reaction attack from a Hunter ranger's Giant Killer feature?

How to preserve electronics (computers, iPads and phones) for hundreds of years

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

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 1 hour ago

uhohuhoh

1264

$endgroup$

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 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

share|cite|improve this question

asked 1 hour ago

uhohuhoh

1264

$endgroup$

share|cite|improve this question

asked 1 hour ago

uhohuhoh

1264

share|cite|improve this question

asked 1 hour ago

uhohuhoh

1264

asked 1 hour ago

uhohuhoh

1264

asked 1 hour ago

uhohuhoh

1264

1 Answer
1

active

oldest

votes

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 57 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
});


}
});

draft saved

draft discarded

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

Name

Email

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

Name

Email

Required, but never shown

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 57 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 57 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 57 mins ago

Harvey MotulskyHarvey Motulsky

10.9k44485

$endgroup$

share|cite|improve this answer

answered 57 mins ago

Harvey MotulskyHarvey Motulsky

10.9k44485

answered 57 mins ago

Harvey MotulskyHarvey Motulsky

10.9k44485

answered 57 mins ago

Harvey MotulskyHarvey Motulsky

10.9k44485