Hypothesis test (binomial) problem

In summary, Hester suspected a biased die and conducted a hypothesis test with a 5% level of significance, obtaining a p-value of 3%. Therefore, the null hypothesis of the die being fair is rejected and it can be concluded that the die is biased in favor of the number four. The other person in the conversation agrees with this conclusion.
  • #1
coconut62
161
1

Homework Statement



Hester suspected that a die was biased in favour of a four occurring. She decided to carry out a hypothesis test. When she threw the die 15 times, she obtained a four on 6 occasions. Carry out the test, at the 5% level, stating your conclusion clearly.

Homework Equations


None.


The Attempt at a Solution



Ho: p = 1/6
H1: p > 1/6

If Ho is true, then X~B (15, 1/6)
Reject Ho if P (X >= 6) < 5%

P (X >= 6) = 1 - P(X <=5)

= 1- (5/6)^15 - (15C1)(1/6)(5/6)^14 - (15C2)(1/6)^2 (5/6)^13 - (15C3)(1/6)^3 (5/6) ^12 -(15C4)(1/6)^4 (5/6)^11 -(15C5)(1/6)^5 (5/6)^10

=0.0274
=3% (1 s.f.)

Since P (X>=6) < 5%, reject Ho.

The die is biased in favour of 4.

(Answer: no evidence that shows die is biased)

Why am I wrong?
 
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  • #2
In my view, you're right. The answer must be wrong.
 
  • #3
PeroK said:
In my view, you're right. The answer must be wrong.

I agree.
 
  • #4
Thanks.
 

Related to Hypothesis test (binomial) problem

1. What is a binomial hypothesis test?

A binomial hypothesis test is a statistical method used to determine whether a given proportion or percentage is significantly different from a specified value or between two or more groups. It is commonly used in experiments involving two categorical variables, such as success or failure.

2. How is a binomial hypothesis test different from other types of hypothesis tests?

Unlike other types of hypothesis tests, a binomial hypothesis test focuses on the number of successes or failures within a fixed number of trials. This makes it suitable for experiments with a binary outcome, where the data can be divided into two categories.

3. What is the process for conducting a binomial hypothesis test?

The first step is to state the null and alternative hypotheses, which represent the current belief and the proposed alternative, respectively. Then, a significance level is chosen to determine the level of evidence needed to reject the null hypothesis. Next, data is collected and a test statistic is calculated. Finally, the test statistic is compared to a critical value or p-value to determine whether the null hypothesis should be rejected or not.

4. What is a type I error in a binomial hypothesis test?

A type I error, also known as a false positive, occurs when the null hypothesis is rejected even though it is actually true. This means that the data suggests a significant difference when there is none in reality. The probability of making a type I error is equal to the chosen significance level.

5. How can I interpret the results of a binomial hypothesis test?

If the null hypothesis is rejected, it means that there is sufficient evidence to support the alternative hypothesis. This can be interpreted as a statistically significant difference between the observed data and the expected results. On the other hand, if the null hypothesis is not rejected, it means that there is not enough evidence to support the alternative hypothesis. This does not necessarily mean that the null hypothesis is true, but rather that there is not enough evidence to suggest otherwise.

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