#StackBounty: #hypothesis-testing #t-test #p-value Estimating "population p-value" $Pi$ using an observed p-value

Bounty: 100

I asked a similar question last month, but from the responses, I see how the question can be asked more precisely.

Let’s suppose a population of the form

$$X sim mathcal{N}(100 + t_{n-1} times sigma / sqrt{n}, sigma)$$

in which $t_{n-1}$ is the student $t$ quantile based on a specific value of a parameter $Pi$ ($0<Pi<1)$. For the sake of the illustration, we could suppose that $Pi$ is 0.025.

When performing a one-sided $t$ test of the null hypothesis $H_0: mu = 100$ on a sample taken from that population, the expected $p$ value is $Pi$, irrespective of sample size (as long as simple randomized sampling is used).

I have 4 questions:

  1. Is the $p$ value a maximum likelihood estimator (MLE) of $Pi$? (Conjecture: yes, because it is based on a $t$ statistic which is based on a likelihood ratio test);

  2. Is the $p$ value a biased estimator of $Pi$? (Conjecture: yes because (i) MLE tend to be biased, and (2) based on simulations, I noted that the median value of many $p$s is close to $Pi$ but the mean value of many $p$s is much larger);

  3. Is the $p$ value a minimum variance estimate of $Pi$? (Conjecture: yes in the asymptotic case but no guarantee for a given sample size)

  4. Can we get a confidence interval around a given $p$ value by using the confidence interval of the observed $t$ value (this is done using the non-central student $t$ distribution with degree of freedom $n-1$ and non-centrality parameter $t$) and computing the $p$ values of the lower and upper bound $t$ values? (Conjecture: yes because both the non-central student $t$ quantiles and the $p$ values of a one-sided test are continuous increasing functions)


Get this bounty!!!

#StackBounty: #hypothesis-testing #t-test #p-value Estimating "population p-value" $Pi$ using an observed p-value

Bounty: 100

I asked a similar question last month, but from the responses, I see how the question can be asked more precisely.

Let’s suppose a population of the form

$$X sim mathcal{N}(100 + t_{n-1} times sigma / sqrt{n}, sigma)$$

in which $t_{n-1}$ is the student $t$ quantile based on a specific value of a parameter $Pi$ ($0<Pi<1)$. For the sake of the illustration, we could suppose that $Pi$ is 0.025.

When performing a one-sided $t$ test of the null hypothesis $H_0: mu = 100$ on a sample taken from that population, the expected $p$ value is $Pi$, irrespective of sample size (as long as simple randomized sampling is used).

I have 4 questions:

  1. Is the $p$ value a maximum likelihood estimator (MLE) of $Pi$? (Conjecture: yes, because it is based on a $t$ statistic which is based on a likelihood ratio test);

  2. Is the $p$ value a biased estimator of $Pi$? (Conjecture: yes because (i) MLE tend to be biased, and (2) based on simulations, I noted that the median value of many $p$s is close to $Pi$ but the mean value of many $p$s is much larger);

  3. Is the $p$ value a minimum variance estimate of $Pi$? (Conjecture: yes in the asymptotic case but no guarantee for a given sample size)

  4. Can we get a confidence interval around a given $p$ value by using the confidence interval of the observed $t$ value (this is done using the non-central student $t$ distribution with degree of freedom $n-1$ and non-centrality parameter $t$) and computing the $p$ values of the lower and upper bound $t$ values? (Conjecture: yes because both the non-central student $t$ quantiles and the $p$ values of a one-sided test are continuous increasing functions)


Get this bounty!!!

#StackBounty: #hypothesis-testing #t-test #p-value Estimating "population p-value" $Pi$ using an observed p-value

Bounty: 100

I asked a similar question last month, but from the responses, I see how the question can be asked more precisely.

Let’s suppose a population of the form

$$X sim mathcal{N}(100 + t_{n-1} times sigma / sqrt{n}, sigma)$$

in which $t_{n-1}$ is the student $t$ quantile based on a specific value of a parameter $Pi$ ($0<Pi<1)$. For the sake of the illustration, we could suppose that $Pi$ is 0.025.

When performing a one-sided $t$ test of the null hypothesis $H_0: mu = 100$ on a sample taken from that population, the expected $p$ value is $Pi$, irrespective of sample size (as long as simple randomized sampling is used).

I have 4 questions:

  1. Is the $p$ value a maximum likelihood estimator (MLE) of $Pi$? (Conjecture: yes, because it is based on a $t$ statistic which is based on a likelihood ratio test);

  2. Is the $p$ value a biased estimator of $Pi$? (Conjecture: yes because (i) MLE tend to be biased, and (2) based on simulations, I noted that the median value of many $p$s is close to $Pi$ but the mean value of many $p$s is much larger);

  3. Is the $p$ value a minimum variance estimate of $Pi$? (Conjecture: yes in the asymptotic case but no guarantee for a given sample size)

  4. Can we get a confidence interval around a given $p$ value by using the confidence interval of the observed $t$ value (this is done using the non-central student $t$ distribution with degree of freedom $n-1$ and non-centrality parameter $t$) and computing the $p$ values of the lower and upper bound $t$ values? (Conjecture: yes because both the non-central student $t$ quantiles and the $p$ values of a one-sided test are continuous increasing functions)


Get this bounty!!!

#StackBounty: #hypothesis-testing #t-test #p-value Estimating "population p-value" $Pi$ using an observed p-value

Bounty: 100

I asked a similar question last month, but from the responses, I see how the question can be asked more precisely.

Let’s suppose a population of the form

$$X sim mathcal{N}(100 + t_{n-1} times sigma / sqrt{n}, sigma)$$

in which $t_{n-1}$ is the student $t$ quantile based on a specific value of a parameter $Pi$ ($0<Pi<1)$. For the sake of the illustration, we could suppose that $Pi$ is 0.025.

When performing a one-sided $t$ test of the null hypothesis $H_0: mu = 100$ on a sample taken from that population, the expected $p$ value is $Pi$, irrespective of sample size (as long as simple randomized sampling is used).

I have 4 questions:

  1. Is the $p$ value a maximum likelihood estimator (MLE) of $Pi$? (Conjecture: yes, because it is based on a $t$ statistic which is based on a likelihood ratio test);

  2. Is the $p$ value a biased estimator of $Pi$? (Conjecture: yes because (i) MLE tend to be biased, and (2) based on simulations, I noted that the median value of many $p$s is close to $Pi$ but the mean value of many $p$s is much larger);

  3. Is the $p$ value a minimum variance estimate of $Pi$? (Conjecture: yes in the asymptotic case but no guarantee for a given sample size)

  4. Can we get a confidence interval around a given $p$ value by using the confidence interval of the observed $t$ value (this is done using the non-central student $t$ distribution with degree of freedom $n-1$ and non-centrality parameter $t$) and computing the $p$ values of the lower and upper bound $t$ values? (Conjecture: yes because both the non-central student $t$ quantiles and the $p$ values of a one-sided test are continuous increasing functions)


Get this bounty!!!

#StackBounty: #hypothesis-testing #t-test #p-value Estimating "population p-value" $Pi$ using an observed p-value

Bounty: 100

I asked a similar question last month, but from the responses, I see how the question can be asked more precisely.

Let’s suppose a population of the form

$$X sim mathcal{N}(100 + t_{n-1} times sigma / sqrt{n}, sigma)$$

in which $t_{n-1}$ is the student $t$ quantile based on a specific value of a parameter $Pi$ ($0<Pi<1)$. For the sake of the illustration, we could suppose that $Pi$ is 0.025.

When performing a one-sided $t$ test of the null hypothesis $H_0: mu = 100$ on a sample taken from that population, the expected $p$ value is $Pi$, irrespective of sample size (as long as simple randomized sampling is used).

I have 4 questions:

  1. Is the $p$ value a maximum likelihood estimator (MLE) of $Pi$? (Conjecture: yes, because it is based on a $t$ statistic which is based on a likelihood ratio test);

  2. Is the $p$ value a biased estimator of $Pi$? (Conjecture: yes because (i) MLE tend to be biased, and (2) based on simulations, I noted that the median value of many $p$s is close to $Pi$ but the mean value of many $p$s is much larger);

  3. Is the $p$ value a minimum variance estimate of $Pi$? (Conjecture: yes in the asymptotic case but no guarantee for a given sample size)

  4. Can we get a confidence interval around a given $p$ value by using the confidence interval of the observed $t$ value (this is done using the non-central student $t$ distribution with degree of freedom $n-1$ and non-centrality parameter $t$) and computing the $p$ values of the lower and upper bound $t$ values? (Conjecture: yes because both the non-central student $t$ quantiles and the $p$ values of a one-sided test are continuous increasing functions)


Get this bounty!!!

#StackBounty: #hypothesis-testing #t-test #p-value Estimating "population p-value" $Pi$ using an observed p-value

Bounty: 100

I asked a similar question last month, but from the responses, I see how the question can be asked more precisely.

Let’s suppose a population of the form

$$X sim mathcal{N}(100 + t_{n-1} times sigma / sqrt{n}, sigma)$$

in which $t_{n-1}$ is the student $t$ quantile based on a specific value of a parameter $Pi$ ($0<Pi<1)$. For the sake of the illustration, we could suppose that $Pi$ is 0.025.

When performing a one-sided $t$ test of the null hypothesis $H_0: mu = 100$ on a sample taken from that population, the expected $p$ value is $Pi$, irrespective of sample size (as long as simple randomized sampling is used).

I have 4 questions:

  1. Is the $p$ value a maximum likelihood estimator (MLE) of $Pi$? (Conjecture: yes, because it is based on a $t$ statistic which is based on a likelihood ratio test);

  2. Is the $p$ value a biased estimator of $Pi$? (Conjecture: yes because (i) MLE tend to be biased, and (2) based on simulations, I noted that the median value of many $p$s is close to $Pi$ but the mean value of many $p$s is much larger);

  3. Is the $p$ value a minimum variance estimate of $Pi$? (Conjecture: yes in the asymptotic case but no guarantee for a given sample size)

  4. Can we get a confidence interval around a given $p$ value by using the confidence interval of the observed $t$ value (this is done using the non-central student $t$ distribution with degree of freedom $n-1$ and non-centrality parameter $t$) and computing the $p$ values of the lower and upper bound $t$ values? (Conjecture: yes because both the non-central student $t$ quantiles and the $p$ values of a one-sided test are continuous increasing functions)


Get this bounty!!!

#StackBounty: #hypothesis-testing #t-test #p-value Estimating "population p-value" $Pi$ using an observed p-value

Bounty: 100

I asked a similar question last month, but from the responses, I see how the question can be asked more precisely.

Let’s suppose a population of the form

$$X sim mathcal{N}(100 + t_{n-1} times sigma / sqrt{n}, sigma)$$

in which $t_{n-1}$ is the student $t$ quantile based on a specific value of a parameter $Pi$ ($0<Pi<1)$. For the sake of the illustration, we could suppose that $Pi$ is 0.025.

When performing a one-sided $t$ test of the null hypothesis $H_0: mu = 100$ on a sample taken from that population, the expected $p$ value is $Pi$, irrespective of sample size (as long as simple randomized sampling is used).

I have 4 questions:

  1. Is the $p$ value a maximum likelihood estimator (MLE) of $Pi$? (Conjecture: yes, because it is based on a $t$ statistic which is based on a likelihood ratio test);

  2. Is the $p$ value a biased estimator of $Pi$? (Conjecture: yes because (i) MLE tend to be biased, and (2) based on simulations, I noted that the median value of many $p$s is close to $Pi$ but the mean value of many $p$s is much larger);

  3. Is the $p$ value a minimum variance estimate of $Pi$? (Conjecture: yes in the asymptotic case but no guarantee for a given sample size)

  4. Can we get a confidence interval around a given $p$ value by using the confidence interval of the observed $t$ value (this is done using the non-central student $t$ distribution with degree of freedom $n-1$ and non-centrality parameter $t$) and computing the $p$ values of the lower and upper bound $t$ values? (Conjecture: yes because both the non-central student $t$ quantiles and the $p$ values of a one-sided test are continuous increasing functions)


Get this bounty!!!

#StackBounty: #hypothesis-testing #t-test #p-value Estimating "population p-value" $Pi$ using an observed p-value

Bounty: 100

I asked a similar question last month, but from the responses, I see how the question can be asked more precisely.

Let’s suppose a population of the form

$$X sim mathcal{N}(100 + t_{n-1} times sigma / sqrt{n}, sigma)$$

in which $t_{n-1}$ is the student $t$ quantile based on a specific value of a parameter $Pi$ ($0<Pi<1)$. For the sake of the illustration, we could suppose that $Pi$ is 0.025.

When performing a one-sided $t$ test of the null hypothesis $H_0: mu = 100$ on a sample taken from that population, the expected $p$ value is $Pi$, irrespective of sample size (as long as simple randomized sampling is used).

I have 4 questions:

  1. Is the $p$ value a maximum likelihood estimator (MLE) of $Pi$? (Conjecture: yes, because it is based on a $t$ statistic which is based on a likelihood ratio test);

  2. Is the $p$ value a biased estimator of $Pi$? (Conjecture: yes because (i) MLE tend to be biased, and (2) based on simulations, I noted that the median value of many $p$s is close to $Pi$ but the mean value of many $p$s is much larger);

  3. Is the $p$ value a minimum variance estimate of $Pi$? (Conjecture: yes in the asymptotic case but no guarantee for a given sample size)

  4. Can we get a confidence interval around a given $p$ value by using the confidence interval of the observed $t$ value (this is done using the non-central student $t$ distribution with degree of freedom $n-1$ and non-centrality parameter $t$) and computing the $p$ values of the lower and upper bound $t$ values? (Conjecture: yes because both the non-central student $t$ quantiles and the $p$ values of a one-sided test are continuous increasing functions)


Get this bounty!!!

#StackBounty: #hypothesis-testing #t-test #p-value Estimating "population p-value" $Pi$ using an observed p-value

Bounty: 100

I asked a similar question last month, but from the responses, I see how the question can be asked more precisely.

Let’s suppose a population of the form

$$X sim mathcal{N}(100 + t_{n-1} times sigma / sqrt{n}, sigma)$$

in which $t_{n-1}$ is the student $t$ quantile based on a specific value of a parameter $Pi$ ($0<Pi<1)$. For the sake of the illustration, we could suppose that $Pi$ is 0.025.

When performing a one-sided $t$ test of the null hypothesis $H_0: mu = 100$ on a sample taken from that population, the expected $p$ value is $Pi$, irrespective of sample size (as long as simple randomized sampling is used).

I have 4 questions:

  1. Is the $p$ value a maximum likelihood estimator (MLE) of $Pi$? (Conjecture: yes, because it is based on a $t$ statistic which is based on a likelihood ratio test);

  2. Is the $p$ value a biased estimator of $Pi$? (Conjecture: yes because (i) MLE tend to be biased, and (2) based on simulations, I noted that the median value of many $p$s is close to $Pi$ but the mean value of many $p$s is much larger);

  3. Is the $p$ value a minimum variance estimate of $Pi$? (Conjecture: yes in the asymptotic case but no guarantee for a given sample size)

  4. Can we get a confidence interval around a given $p$ value by using the confidence interval of the observed $t$ value (this is done using the non-central student $t$ distribution with degree of freedom $n-1$ and non-centrality parameter $t$) and computing the $p$ values of the lower and upper bound $t$ values? (Conjecture: yes because both the non-central student $t$ quantiles and the $p$ values of a one-sided test are continuous increasing functions)


Get this bounty!!!