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Type 1 Error & Type 2 Error's pregnancy test analogy: is it legit?
The 2019 Stack Overflow Developer Survey Results Are InGoldfeld - Quandt test statistic equal to 1Test of two variance ratios being equalChristiano Fitzgerald filtering processWhat is the standard error on quarterly GDP figure?Using the sample mean to test hypothesesIf someone stays at home because they can't find the type of job they want, are they included in unemployment numbers?Multivariate linear regression: how to test for whether the slopes are the same?
$begingroup$
I found this picture in my stats book but I'm now confused to what 'positive' and 'negative' is referring to.
As seen in the table below, Type 1 error is the error that its H0 is actually true but FALSEly claims that it's false. Type 2 error, on the other hand, is the error that its H0 is actually false but FALSEly claims that it's true.
So my question is, how do the pregnancy analogy and whole 'false positive' & 'false negative' thing make sense?
For the first picture to be a type 1 error, H0 should be "The person is NOT pregnant" so that "You're pregnant" statement becomes false.
However, the second picture has the complete opposite H0, where H0 should be "The person is pregnant" so that "You're not pregnant" statement becomes false.
I thought it was really confusing because I thought false POSITIVE and false NEGATIVE corresponded to "You're pregnant"(positive) / "You're NOT pregnant"(negative)
But based on the table below, that doesn't seem to make any sense.
So the question is, is there anything that I'm missing here or is it just that textbook's analogy sucks?
statistics
asked Apr 7 at 11:53
user8491363user8491363
182
New contributor
user8491363 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I found this picture in my stats book but I'm now confused to what 'positive' and 'negative' is referring to.
As seen in the table below, Type 1 error is the error that its H0 is actually true but FALSEly claims that it's false. Type 2 error, on the other hand, is the error that its H0 is actually false but FALSEly claims that it's true.
So my question is, how do the pregnancy analogy and whole 'false positive' & 'false negative' thing make sense?
For the first picture to be a type 1 error, H0 should be "The person is NOT pregnant" so that "You're pregnant" statement becomes false.
However, the second picture has the complete opposite H0, where H0 should be "The person is pregnant" so that "You're not pregnant" statement becomes false.
I thought it was really confusing because I thought false POSITIVE and false NEGATIVE corresponded to "You're pregnant"(positive) / "You're NOT pregnant"(negative)
But based on the table below, that doesn't seem to make any sense.
So the question is, is there anything that I'm missing here or is it just that textbook's analogy sucks?
statistics
asked Apr 7 at 11:53
user8491363user8491363
182
New contributor
user8491363 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I found this picture in my stats book but I'm now confused to what 'positive' and 'negative' is referring to.
As seen in the table below, Type 1 error is the error that its H0 is actually true but FALSEly claims that it's false. Type 2 error, on the other hand, is the error that its H0 is actually false but FALSEly claims that it's true.
So my question is, how do the pregnancy analogy and whole 'false positive' & 'false negative' thing make sense?
For the first picture to be a type 1 error, H0 should be "The person is NOT pregnant" so that "You're pregnant" statement becomes false.
However, the second picture has the complete opposite H0, where H0 should be "The person is pregnant" so that "You're not pregnant" statement becomes false.
I thought it was really confusing because I thought false POSITIVE and false NEGATIVE corresponded to "You're pregnant"(positive) / "You're NOT pregnant"(negative)
But based on the table below, that doesn't seem to make any sense.
So the question is, is there anything that I'm missing here or is it just that textbook's analogy sucks?
statistics
asked Apr 7 at 11:53
user8491363user8491363
182
New contributor
user8491363 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I found this picture in my stats book but I'm now confused to what 'positive' and 'negative' is referring to.
As seen in the table below, Type 1 error is the error that its H0 is actually true but FALSEly claims that it's false. Type 2 error, on the other hand, is the error that its H0 is actually false but FALSEly claims that it's true.
So my question is, how do the pregnancy analogy and whole 'false positive' & 'false negative' thing make sense?
For the first picture to be a type 1 error, H0 should be "The person is NOT pregnant" so that "You're pregnant" statement becomes false.
However, the second picture has the complete opposite H0, where H0 should be "The person is pregnant" so that "You're not pregnant" statement becomes false.
I thought it was really confusing because I thought false POSITIVE and false NEGATIVE corresponded to "You're pregnant"(positive) / "You're NOT pregnant"(negative)
But based on the table below, that doesn't seem to make any sense.
So the question is, is there anything that I'm missing here or is it just that textbook's analogy sucks?
statistics
statistics
asked Apr 7 at 11:53
user8491363user8491363
182
New contributor
user8491363 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Apr 7 at 11:53
user8491363user8491363
182
New contributor
user8491363 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Apr 7 at 11:53
user8491363user8491363
182
New contributor
user8491363 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked Apr 7 at 11:53
user8491363user8491363
182
asked Apr 7 at 11:53
user8491363user8491363
182
182
New contributor
user8491363 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
user8491363 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
user8491363 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
Presumably here
- the null hypothesis is $H_0:$ You are not pregnant
- the alternative hypothesis is $H_1:$ You are pregnant
so being pregnant would be the positive result.
You take a pregnancy test
if the pregnancy test gives a positive result when you are not pregnant then this is a false positive, a Type I error when the null hypothesis $H_0$ is in fact true but has been rejected by the test
if the pregnancy test gives a negative result when you are pregnant then this is a false negative, a Type II error when the null hypothesis $H_0$ is in fact untrue but has not been rejected by the test
So in a statement of being a true/false positive/negative test, the true/false part is about the accuracy of the test while the positive/negative part is about the result of the test rather than being the real situation
answered Apr 7 at 12:06
HenryHenry
3,826316
$endgroup$
$begingroup$
This is very clear. Thank you, Henry. I must have been confused at some point. One more question if it's okay though: is it normal to set H0 as ~ is NOT true rather than ~ is true? Say, if I wanna know whether an economic theory is right, is it normal to set H0 as "Theory A is NOT true" rather than "~ is true"?
$endgroup$
– user8491363
Apr 8 at 0:39
$begingroup$
@user8491363 The null hypothesis is often formed as a no-change statement, such as "using this experimental drug does not affect survival rates" or "knowing variable X does not change the ability to predict variable Y" or in this example "the patient continues not to be pregnant". The alternative hypothesis then points towards what sort of evidence might be deemed significant enough to reject the null hypothesis.
$endgroup$
– Henry
2 days ago
$begingroup$
Thank you again. It is much clear noe.
$endgroup$
– user8491363
2 days ago
add a comment |
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$begingroup$
Presumably here
- the null hypothesis is $H_0:$ You are not pregnant
- the alternative hypothesis is $H_1:$ You are pregnant
so being pregnant would be the positive result.
You take a pregnancy test
if the pregnancy test gives a positive result when you are not pregnant then this is a false positive, a Type I error when the null hypothesis $H_0$ is in fact true but has been rejected by the test
if the pregnancy test gives a negative result when you are pregnant then this is a false negative, a Type II error when the null hypothesis $H_0$ is in fact untrue but has not been rejected by the test
So in a statement of being a true/false positive/negative test, the true/false part is about the accuracy of the test while the positive/negative part is about the result of the test rather than being the real situation
answered Apr 7 at 12:06
HenryHenry
3,826316
$endgroup$
$begingroup$
This is very clear. Thank you, Henry. I must have been confused at some point. One more question if it's okay though: is it normal to set H0 as ~ is NOT true rather than ~ is true? Say, if I wanna know whether an economic theory is right, is it normal to set H0 as "Theory A is NOT true" rather than "~ is true"?
$endgroup$
– user8491363
Apr 8 at 0:39
$begingroup$
@user8491363 The null hypothesis is often formed as a no-change statement, such as "using this experimental drug does not affect survival rates" or "knowing variable X does not change the ability to predict variable Y" or in this example "the patient continues not to be pregnant". The alternative hypothesis then points towards what sort of evidence might be deemed significant enough to reject the null hypothesis.
$endgroup$
– Henry
2 days ago
$begingroup$
Thank you again. It is much clear noe.
$endgroup$
– user8491363
2 days ago
add a comment |
$begingroup$
Presumably here
- the null hypothesis is $H_0:$ You are not pregnant
- the alternative hypothesis is $H_1:$ You are pregnant
so being pregnant would be the positive result.
You take a pregnancy test
if the pregnancy test gives a positive result when you are not pregnant then this is a false positive, a Type I error when the null hypothesis $H_0$ is in fact true but has been rejected by the test
if the pregnancy test gives a negative result when you are pregnant then this is a false negative, a Type II error when the null hypothesis $H_0$ is in fact untrue but has not been rejected by the test
So in a statement of being a true/false positive/negative test, the true/false part is about the accuracy of the test while the positive/negative part is about the result of the test rather than being the real situation
answered Apr 7 at 12:06
HenryHenry
3,826316
$endgroup$
$begingroup$
This is very clear. Thank you, Henry. I must have been confused at some point. One more question if it's okay though: is it normal to set H0 as ~ is NOT true rather than ~ is true? Say, if I wanna know whether an economic theory is right, is it normal to set H0 as "Theory A is NOT true" rather than "~ is true"?
$endgroup$
– user8491363
Apr 8 at 0:39
$begingroup$
@user8491363 The null hypothesis is often formed as a no-change statement, such as "using this experimental drug does not affect survival rates" or "knowing variable X does not change the ability to predict variable Y" or in this example "the patient continues not to be pregnant". The alternative hypothesis then points towards what sort of evidence might be deemed significant enough to reject the null hypothesis.
$endgroup$
– Henry
2 days ago
$begingroup$
Thank you again. It is much clear noe.
$endgroup$
– user8491363
2 days ago
add a comment |
$begingroup$
Presumably here
- the null hypothesis is $H_0:$ You are not pregnant
- the alternative hypothesis is $H_1:$ You are pregnant
so being pregnant would be the positive result.
You take a pregnancy test
if the pregnancy test gives a positive result when you are not pregnant then this is a false positive, a Type I error when the null hypothesis $H_0$ is in fact true but has been rejected by the test
if the pregnancy test gives a negative result when you are pregnant then this is a false negative, a Type II error when the null hypothesis $H_0$ is in fact untrue but has not been rejected by the test
So in a statement of being a true/false positive/negative test, the true/false part is about the accuracy of the test while the positive/negative part is about the result of the test rather than being the real situation
answered Apr 7 at 12:06
HenryHenry
3,826316
$endgroup$
Presumably here
- the null hypothesis is $H_0:$ You are not pregnant
- the alternative hypothesis is $H_1:$ You are pregnant
so being pregnant would be the positive result.
You take a pregnancy test
if the pregnancy test gives a positive result when you are not pregnant then this is a false positive, a Type I error when the null hypothesis $H_0$ is in fact true but has been rejected by the test
if the pregnancy test gives a negative result when you are pregnant then this is a false negative, a Type II error when the null hypothesis $H_0$ is in fact untrue but has not been rejected by the test
So in a statement of being a true/false positive/negative test, the true/false part is about the accuracy of the test while the positive/negative part is about the result of the test rather than being the real situation
answered Apr 7 at 12:06
HenryHenry
3,826316
answered Apr 7 at 12:06
HenryHenry
3,826316
answered Apr 7 at 12:06
HenryHenry
3,826316
answered Apr 7 at 12:06
HenryHenry
3,826316
3,826316
$begingroup$
This is very clear. Thank you, Henry. I must have been confused at some point. One more question if it's okay though: is it normal to set H0 as ~ is NOT true rather than ~ is true? Say, if I wanna know whether an economic theory is right, is it normal to set H0 as "Theory A is NOT true" rather than "~ is true"?
$endgroup$
– user8491363
Apr 8 at 0:39
$begingroup$
@user8491363 The null hypothesis is often formed as a no-change statement, such as "using this experimental drug does not affect survival rates" or "knowing variable X does not change the ability to predict variable Y" or in this example "the patient continues not to be pregnant". The alternative hypothesis then points towards what sort of evidence might be deemed significant enough to reject the null hypothesis.
$endgroup$
– Henry
2 days ago
$begingroup$
Thank you again. It is much clear noe.
$endgroup$
– user8491363
2 days ago
add a comment |
$begingroup$
This is very clear. Thank you, Henry. I must have been confused at some point. One more question if it's okay though: is it normal to set H0 as ~ is NOT true rather than ~ is true? Say, if I wanna know whether an economic theory is right, is it normal to set H0 as "Theory A is NOT true" rather than "~ is true"?
$endgroup$
– user8491363
Apr 8 at 0:39
$begingroup$
@user8491363 The null hypothesis is often formed as a no-change statement, such as "using this experimental drug does not affect survival rates" or "knowing variable X does not change the ability to predict variable Y" or in this example "the patient continues not to be pregnant". The alternative hypothesis then points towards what sort of evidence might be deemed significant enough to reject the null hypothesis.
$endgroup$
– Henry
2 days ago
$begingroup$
Thank you again. It is much clear noe.
$endgroup$
– user8491363
2 days ago
$begingroup$
This is very clear. Thank you, Henry. I must have been confused at some point. One more question if it's okay though: is it normal to set H0 as ~ is NOT true rather than ~ is true? Say, if I wanna know whether an economic theory is right, is it normal to set H0 as "Theory A is NOT true" rather than "~ is true"?
$endgroup$
– user8491363
Apr 8 at 0:39
$begingroup$
This is very clear. Thank you, Henry. I must have been confused at some point. One more question if it's okay though: is it normal to set H0 as ~ is NOT true rather than ~ is true? Say, if I wanna know whether an economic theory is right, is it normal to set H0 as "Theory A is NOT true" rather than "~ is true"?
$endgroup$
– user8491363
Apr 8 at 0:39
$begingroup$
@user8491363 The null hypothesis is often formed as a no-change statement, such as "using this experimental drug does not affect survival rates" or "knowing variable X does not change the ability to predict variable Y" or in this example "the patient continues not to be pregnant". The alternative hypothesis then points towards what sort of evidence might be deemed significant enough to reject the null hypothesis.
$endgroup$
– Henry
2 days ago
$begingroup$
@user8491363 The null hypothesis is often formed as a no-change statement, such as "using this experimental drug does not affect survival rates" or "knowing variable X does not change the ability to predict variable Y" or in this example "the patient continues not to be pregnant". The alternative hypothesis then points towards what sort of evidence might be deemed significant enough to reject the null hypothesis.
$endgroup$
– Henry
2 days ago
$begingroup$
Thank you again. It is much clear noe.
$endgroup$
– user8491363
2 days ago
$begingroup$
Thank you again. It is much clear noe.
$endgroup$
– user8491363
2 days ago
add a comment |
user8491363 is a new contributor. Be nice, and check out our Code of Conduct.
user8491363 is a new contributor. Be nice, and check out our Code of Conduct.
user8491363 is a new contributor. Be nice, and check out our Code of Conduct.
user8491363 is a new contributor. Be nice, and check out our Code of Conduct.
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