To determine the implied null and alternative hypotheses, consider what the consumer advocacy group aims to demonstrate and test.

Null Hypothesis (H₀):

The null hypothesis reflects the assumption of no effect or no difference and serves as the status quo. In this case: $H₀: \mu = \mu_0$ Where:

• $\mu$ = the true population mean of the product (e.g., weight, size, etc., depending on the context of the problem).
• $\mu_0$ = the stated or advertised mean value by the company.

This hypothesis implies that there is no discrepancy between the population mean and the advertised value.

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Alternative Hypothesis (H₁):

The alternative hypothesis reflects what the consumer advocacy group is interested in proving. Typically: $H₁: \mu \neq \mu_0 \quad \text{(two-tailed test, if deviation in either direction matters)}$ or $H₁: \mu < \mu_0 \quad \text{(one-tailed test, if the group suspects the mean is less than advertised)}$

The exact form depends on whether the group believes the product fails to meet expectations specifically by being lower than the advertised value or if any deviation is concerning.

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Interpretation:

• If the consumer advocacy group is specifically looking to show that the product falls short of expectations, the alternative hypothesis will likely be one-tailed: $\mu < \mu_0$.
• If they are concerned about any deviation, whether above or below the advertised value, then it will be two-tailed: $\mu \neq \mu_0$.

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Do you want further assistance with hypothesis formulation, or should I proceed with an example test?

Follow-up Questions:

1. What is the parameter (mean, proportion, etc.) being tested?
2. What is the assumed value ($\mu_0$) stated by the company or industry standard?
3. Does the group suspect the actual value to be higher, lower, or just different than the claimed value?
4. Should we assume a one-tailed or two-tailed hypothesis for this scenario?
5. What statistical test is appropriate for verifying these hypotheses?

Tip: When forming hypotheses, focus on what the group is trying to prove as the alternative hypothesis and assume the opposite as the null hypothesis.