Testing hypothesis and significance level

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To test the hypothesis that a coin is fair using the number of flips until the first heads (N), a significance test can be developed at an alpha level of 0.1. The null hypothesis (H0) posits that the coin is fair, while the alternative hypothesis (H1) suggests it is biased. The distribution of N follows a geometric distribution, where the expected value for a fair coin is 2. The test involves calculating the probability of observing N under the null hypothesis and comparing it to the alpha level. This approach allows for determining if the observed data significantly deviates from what is expected for a fair coin.
vptran84
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Let N be the number of flips of a coin up to an including the first flip of heads. Develope a significance test for N at the alpha=0.1 to test the hypothesis H that the coin is fair.

Can someone please help me with this statistics problem? I have no clue how to start it. Thank you :smile:
 
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We are covering this in class next week. So stay tuned. :D
 

Thread 'Hypothesis testing: Defining H0, HA hypotheses so that ( H_A)_A' makes sense'

The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...
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