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From the image you provided, we have a hypothesis testing problem regarding the proportion of female fish in a population. The claim is that "at most 54% are female," and a test is being conducted to evaluate this claim.

### Hypotheses Setup:
- **Null Hypothesis (H₀)**: This represents the claim that the proportion of female fish is at most 54%. Mathematically, this is stated as:
  \[
  H_0: p \leq 0.54
  \]
  
- **Alternative Hypothesis (H₁ or Hₐ)**: This is the hypothesis that challenges the null. Since the test is about whether the proportion is greater than 54%, the alternative hypothesis is:
  \[
  H_a: p > 0.54
  \]

Thus, you should fill in the boxes as follows:
- **Null Hypothesis (H₀)**: \( p \leq 0.54 \)
- **Alternative Hypothesis (H₁)**: \( p > 0.54 \)

Do you need further clarification or details on hypothesis testing or the process of setting these up?

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Here are 5 relative questions for further practice:
1. What is the difference between a one-tailed and a two-tailed hypothesis test?
2. How do you determine the appropriate significance level (α) for a hypothesis test?
3. What is a Type I error in hypothesis testing, and how can it be minimized?
4. How do you calculate the test statistic for a proportion hypothesis test?
5. When should you use a z-test for proportions?

**Tip**: In hypothesis testing, always carefully distinguish between the null and alternative hypotheses, especially in one-tailed tests, to ensure your conclusions align with the initial claim.

A scientist claims that in a population of fish, at most 54% are female. A test is conducted to test the claim. Identify the null hypothesis and the alternative hypothesis.

Understand how to set up null and alternative hypotheses for testing a claim about the proportion of female fish in a population. This problem involves hypothesis testing concepts and is suitable for students in Grades 11-12.

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