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To express the null and alternative hypotheses in symbolic form, let’s define \( p \) as the true proportion of workers who got their job through networking. The claim is that **less than 78%** of workers got their job through networking.

### Hypotheses:
1. **Null Hypothesis (\( H_0 \))**: The null hypothesis represents the status quo or the assumption that there is no effect or difference. Here, it assumes that 78% or more of workers got their job through networking.
   \[
   H_0: p = 78\%
   \]

2. **Alternative Hypothesis (\( H_1 \))**: The alternative hypothesis represents the claim that is being tested, which in this case is that less than 78% of workers got their job through networking.
   \[
   H_1: p < 78\%
   \]

### Final Form:
- \( H_0: p = 78 \)
- \( H_1: p < 78 \)

Would you like a more detailed explanation of hypothesis testing?

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Here are some related questions to expand on this concept:

1. How do we determine the significance level when testing hypotheses?
2. What statistical test would be appropriate for testing this hypothesis?
3. How is the p-value used in the context of hypothesis testing?
4. What are Type I and Type II errors in hypothesis testing?
5. How do sample size and variability affect the power of a hypothesis test?

**Tip:** In hypothesis testing, the alternative hypothesis often represents the research hypothesis or claim being tested.

Less than 78% of workers got their job through networking. Express the null and alternative hypotheses in symbolic form for this claim (enter as a percentage).

Explore hypothesis testing for proportions with a focus on workers obtaining jobs through networking. This problem demonstrates setting up null and alternative hypotheses for a claim about worker networking rates, ideal for undergraduate statistics students.

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