What is the Probability of Type I Error for a Uniform Random Variable Test?

In summary, the problem is asking for the value of alpha, which is the probability of rejecting the null hypothesis when it is actually true. This can be calculated by finding the joint pdf of the two observations, which is 1/theta^2. Then, using the joint pdf, the probability of rejecting the null hypothesis can be calculated by integrating over the region where (x1+x2)/2 > .99. This results in a value of 0.98, which is the desired alpha value.
  • #1
safina
28
0

Homework Statement


Given that X is a uniform random variable on the interval [tex](0, \theta)[/tex], we might test [tex]Ho: \theta = 1[/tex] versus the alternative [tex]H_{1}: \theta = 2[/tex] by taking a sample of 2 observations of X and rejecting Ho if [tex]\bar{X} > 0.99[/tex]. Compute [tex]\alpha[/tex]2. The attempt at a solution
[tex]\alpha[/tex] = P[type I error]
= P[rejecting Ho| Ho is true]
= P[[tex]\bar{X} > 0.99 given that \theta = 1][/tex]

I just know that if X is a uniform random variable, it has a pdf:
[tex]f\left(x; a, b\right) = \frac{1}{b - a}I_{[a, b]}(x)[/tex]

Kindly help me what to do next.
 
Physics news on Phys.org
  • #2
So, under H0, what is the joint pdf of X1 and X2? You didn't state it but I'm guessing the two observations are independent. Once you have that you can calculate α directly.
 
  • #3
LCKurtz said:
So, under H0, what is the joint pdf of X1 and X2? You didn't state it but I'm guessing the two observations are independent. Once you have that you can calculate α directly.

the joint pdf of X[tex]_{1}[/tex] and X[tex]_{2}[/tex] is [tex]\frac{1}{\theta}[/tex] [tex]\frac{1}{\theta}[/tex] = [tex]\frac{1}{\theta^{2}}[/tex]
But I still don't get how is it related in getting [tex]\alpha[/tex]
 
  • #4
safina said:
the joint pdf of X[tex]_{1}[/tex] and X[tex]_{2}[/tex] is [tex]\frac{1}{\theta}[/tex] [tex]\frac{1}{\theta}[/tex] = [tex]\frac{1}{\theta^{2}}[/tex]
But I still don't get how is it related in getting [tex]\alpha[/tex]

But under H0, θ = 1, so your joint probability is 1 on the unit square. Do you know how to calculate a probability when you know the joint pdf? You just need to calculate the probability

[tex]P\left(\frac {X_1+X_2}{2} >.99\right)[/tex]
 
  • #5
LCKurtz said:
But under H0, θ = 1, so your joint probability is 1 on the unit square. Do you know how to calculate a probability when you know the joint pdf? You just need to calculate the probability

[tex]P\left(\frac {X_1+X_2}{2} >.99\right)[/tex]

I really am confused how to calculate this probability.
Also I think there is no table for uniform distribution.
 
  • #6
LCKurtz said:
But under H0, θ = 1, so your joint probability is 1 on the unit square. Do you know how to calculate a probability when you know the joint pdf? You just need to calculate the probability

[tex]P\left(\frac {X_1+X_2}{2} >.99\right)[/tex]

safina said:
I really am confused how to calculate this probability.
Also I think there is no table for uniform distribution.

You don't need a table. This problem can be done without calculus, but the normal way to calculate a probability is with an integral. Have you had calculus?

Generally, if X and Y have joint pdf f(x,y) and A is a subset of the sample space

[tex]P(A) = \iint_A f(x,y)\,dxdy[/tex]

In your case

[tex]A =\left\{ (x_1,x_2):\frac{x_1+x_2}{2} > .99 \right\}[/tex]
 
  • #7
LCKurtz said:
You don't need a table. This problem can be done without calculus, but the normal way to calculate a probability is with an integral. Have you had calculus?

Generally, if X and Y have joint pdf f(x,y) and A is a subset of the sample space

[tex]P(A) = \iint_A f(x,y)\,dxdy[/tex]

In your case

[tex]A =\left\{ (x_1,x_2):\frac{x_1+x_2}{2} > .99 \right\}[/tex]

[tex]P\left[\frac{X_{1}+X_{2}}{2} > 0.99\right][/tex] = [tex]\int^{1}_{0}\int^{0.98}_{0} 1 dx_{1}dx_{2}[/tex] = 0.98

Is this right?
If I double integrate the joint pdf with when X1 goes from 0 to 1 and X2 goes from 0 to 1, I got [tex]P\left[\frac{X_{1}+X_{2}}{2} > 0.99\right][/tex] = 1.

I think it is not right because it is too high to be a value of [tex]\alpha[/tex]
 
  • #8
safina said:
[tex]P\left[\frac{X_{1}+X_{2}}{2} > 0.99\right][/tex] = [tex]\int^{1}_{0}\int^{0.98}_{0} 1 dx_{1}dx_{2}[/tex] = 0.98

Is this right?
If I double integrate the joint pdf with when X1 goes from 0 to 1 and X2 goes from 0 to 1, I got [tex]P\left[\frac{X_{1}+X_{2}}{2} > 0.99\right][/tex] = 1.

I think it is not right because it is too high to be a value of [tex]\alpha[/tex]

You are correct thinking that isn't right. You need to draw a picture of the unit square and shade the portion of it where (x1+x2)/2 > .99. Then integrate over that region. Hint: It isn't a rectangle, which your attempt is, having constant limits.
 

FAQ: What is the Probability of Type I Error for a Uniform Random Variable Test?

What is the probability of type I error?

The probability of type I error, also known as a false positive, is the likelihood of rejecting a true null hypothesis. It is typically denoted by the symbol alpha (α), and is set by the researcher before conducting a hypothesis test.

How is the probability of type I error determined?

The probability of type I error is determined by the significance level or alpha (α) that is chosen by the researcher. This value is typically set at 5% or 0.05, but can vary depending on the specific research or experiment being conducted.

What factors can affect the probability of type I error?

The probability of type I error can be affected by the chosen significance level, sample size, and the variability of the data being analyzed. A higher significance level or smaller sample size can increase the chances of making a type I error, while a lower significance level or larger sample size can decrease the chances.

Why is controlling the probability of type I error important?

Controlling the probability of type I error is important because it helps to ensure the validity and reliability of research results. Making a false positive conclusion can lead to incorrect conclusions and wasted resources, which can ultimately harm the credibility of the research.

How can the probability of type I error be reduced?

The probability of type I error can be reduced by choosing a lower significance level, increasing the sample size, and conducting multiple tests to confirm results. Additionally, careful planning and design of experiments can help to minimize the chances of making a type I error.

Similar threads

Back
Top