Solve the problem that involves Binomial distribution and hypothesis test

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In summary, solving a problem that involves Binomial distribution and hypothesis testing requires understanding the characteristics of the Binomial distribution, such as the number of trials, the probability of success, and the resulting probability mass function. The hypothesis test typically involves formulating a null hypothesis and an alternative hypothesis, calculating the test statistic based on the observed data, and determining the p-value or critical value to make a decision. The outcome helps assess whether the observed results significantly differ from what would be expected under the null hypothesis, thereby allowing for informed conclusions about the data.
  • #1
chwala
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Homework Statement
see attached
Relevant Equations
Binomial distribution
For this problem, the steps are quite clear. My question would be on the highlighted part, clearly we are told ##n=2## as claim from supplier then why did they consider other ##n## values? that is, ##n=1## and ##n=0## to give us,

##[P,X=2] = 0.002918+0.017764 + 0.053292 = 0.07397562##

why did they not use the value for ##n=2## as it is? Maybe, I am not getting the word problem clearly...

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  • #2
chwala said:
why did they not use the value for n=2 as it is? Maybe, I am not getting the word problem clearly...
Because P(D = 2) (i.e., exactly 2 damp bags) is part (a), subpart (i). Subpart (ii) asks you to find P(D > 3).
 
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  • #3
Mark44 said:
Because P(D = 2) (i.e., exactly 2 damp bags) is part (a), subpart (i). Subpart (ii) asks you to find P(D > 3).
Sorry my question is on question 2(b) only. I have no problem with 2 (i) and 2(ii). This part is easy for me.

This part is fine,

For 2(i), i have

##{_{35}C_2} (0.08)^2 (0.92)^{33} = 0.243##

For part (ii) i have,

##P(D>3) = 1 - [0.054+0.1644+0.2430+0.2324]##
##P(D>3) = 1 - 0.6938=0.3062##

My question is on 2(b)
They clearly used

##[P,X=2] = 0.002918+0.017764 + 0.053292 = 0.07397562##


This is where my problem is as the question states only 2 of the bags are damp.

Why not ##[P,X=2] = 0.05329##
 
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  • #4
I don't understand what their notation is saying, for example [P(X ,, 2)] or in the work for 2a(ii) where they have [1 - P(D ,, 3) = ...]

Also, it looks like the answer key has a typo in the second line of 2b:
[X~]B(70, 0.08)
I believe this should be [X ~ B(70, 0.08)], meaning that X is a random variable with a binomial distribution with n = 70 and p = 0.08.
,
 
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  • #5
Mark44 said:
I don't understand what their notation is saying, for example [P(X ,, 2)] or in the work for 2a(ii) where they have [1 - P(D ,, 3) = ...]

Also, it looks like the answer key has a typo in the second line of 2b:

I believe this should be [X ~ B(70, 0.08)}, meaning that X is a random variable with a binomial distribution with n = 70 and p = 0.08.
,
Correct, that is a typo.
 
  • #6
Do you understand what they mean by [P(X ,, 2)]? Do they explain this notation elsewhere in the textbook?
 
  • #7
Mark44 said:
Do you understand what they mean by [P(X ,, 2)]? Do they explain this notation elsewhere in the textbook?
I do not understand.

This question is from an A-level past paper question.
 
  • #8
Mark44 said:
Do you understand what they mean by [P(X ,, 2)]? Do they explain this notation elsewhere in the textbook?
... but going with the mark scheme guide, that should not be difficult to determine. It is simply a sum of combinations that is, P[X=0 ] + P[X=1 ] + P[X=2] = P[X ,,2].
 
  • #9
chwala said:
They clearly used
##[P,X=2] = 0.002918+0.017764 + 0.053292 = 0.07397562##

This is where my problem is as the question states only 2 of the bags are damp.

Why not ##[P,X=2] = 0.05329##
I believe the idea here is that they're calculating the probability ##P(X \le 2)##, which means they need to calculate the sum ##P(X = 0) + P(X = 1) + P(X - 2)##. I get the same numbers you did and that are shown in the mark scheme.
Like I said, their notation leaves something to be desired. It would be clearer if they wrote ##P(X \le 2)## or maybe [P, X=0...2] with the understanding that X ~ B(70, .08). Keep in mind that the hypothesis test is whether p = 0.08 vs the alternate hypothesis that p < 0.08.
 
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  • #10
I don't think you understood my question. The way they worked the problem to solution is quite clear @Mark44

I just need to understand the highlighted part in post ##3##.
 
  • #11
chwala said:
I don't think you understood my question.
I don't think you understood the question for 2b.
A candy manufacturer makes the claim that 8% of the bags of sugar that are received from the supplier are wet. The supplier disputes that claim, and instead asserts that the proportion of wet bags of sugar is less than 8%.
The supplier sets up a hypothesis test to determine a confidence interval with ##\alpha = 0.10## for the true value of p, the proportion of wet sugar bags.
##H_0 : p = 0.08## -- The manufacturer's claim
##H_a : p < 0.08## -- The supplier's claim

The null hypothesis is equivalent to ##P(D = 2)##. The alternate hypothesis is equivalent to ##P(D \le 2)## which in turn is equal to ##P(D = 0) + P(D = 1) + P(D = 2)##. If this probability turns out to be less than 0.10, we reject the null hypothesis because we're way into the left tail of the distribution. If that probability turns out to be 0.10 or larger, we accept the null hypothesis.

As you have already found, ##P(D \le 2) \approx 0.074##, which implies that in actuality, fewer than 8% of the bags of sugar are wet.
 
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FAQ: Solve the problem that involves Binomial distribution and hypothesis test

What is a Binomial distribution?

A Binomial distribution is a discrete probability distribution that describes the number of successes in a fixed number of independent Bernoulli trials, each with the same probability of success. It is characterized by two parameters: the number of trials (n) and the probability of success (p). The probability mass function is given by the formula P(X = k) = (n choose k) * p^k * (1-p)^(n-k), where k is the number of successes.

How do you know if a problem can be solved using a Binomial distribution?

A problem can be solved using a Binomial distribution if it meets the following criteria: there are a fixed number of trials (n), each trial has two possible outcomes (success or failure), the trials are independent, and the probability of success (p) remains constant across trials. If these conditions are satisfied, the Binomial distribution is appropriate for modeling the situation.

What is a hypothesis test in the context of Binomial distribution?

A hypothesis test in the context of Binomial distribution is a statistical method used to determine whether there is enough evidence to reject a null hypothesis regarding a binomial parameter, typically the probability of success (p). It involves formulating a null hypothesis (H0) and an alternative hypothesis (H1), calculating the test statistic based on observed data, and comparing this statistic to a critical value or using a p-value to make a decision.

How do you perform a hypothesis test for a Binomial distribution?

To perform a hypothesis test for a Binomial distribution, follow these steps: 1) Define the null hypothesis (H0) and alternative hypothesis (H1). 2) Determine the significance level (α). 3) Collect data and calculate the number of successes (k) in n trials. 4) Calculate the test statistic using the Binomial probability formula or normal approximation if n is large. 5) Compare the test statistic to the critical value from the Binomial distribution or calculate the p-value. 6) Make a decision to reject or fail to reject H0 based on the comparison.

What are some common pitfalls when using Binomial distribution and hypothesis testing?

Common pitfalls include not verifying the assumptions of the Binomial distribution (fixed number of trials, independence, constant probability), miscalculating probabilities or test statistics, using the wrong critical values or p-values, and failing to consider the context of the data when interpreting results. Additionally, it is important to ensure that the sample size is adequate to provide reliable results, especially when using normal approximation methods.

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