In hypothesis testing, the null hypothesis ($H_0$) typically represents a statement of no effect or no difference, while the alternative hypothesis ($H_a$) represents the statement we are testing for. Based on the information provided, here's how the hypotheses would be structured:

Null Hypothesis ($H_0$):

The null hypothesis represents the claim that the mean weight of flounders is not greater than the specified value. $H_0: \mu \leq \text{value in grams}$ (Where $\mu$ is the population mean weight of the flounder.)

Alternative Hypothesis ($H_a$):

The alternative hypothesis is what scientists are testing for, which is that the mean weight is greater than the specified value. $H_a: \mu > \text{value in grams}$ (Where $\mu$ is again the population mean weight of the flounder.)

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Would you like further explanation on the hypothesis testing process, such as calculating test statistics or p-values?

Here are 5 related questions for you:

1. What is the significance level ($\alpha$) in hypothesis testing, and how do you choose it?
2. How would you calculate the test statistic for a hypothesis test on the mean?
3. What is a p-value, and how do you interpret it in hypothesis testing?
4. How do you determine whether to reject or fail to reject the null hypothesis?
5. What assumptions do you need to make when performing a hypothesis test about a population mean?

Tip: Always check the sample size and normality assumptions (e.g., using the Central Limit Theorem) before applying certain statistical tests.