1. **State the problem:** We want to test if the new typing exercise method has a better success rate than the traditional method. The probability of success is $p$. 2. **Set hypotheses:** - Null hypothesis $H_0$: $p = 0.7$ (new method is the same as traditional) - Alternative hypothesis $H_1$: $p > 0.7$ (new method is better) 3. **Significance level:** We use $\alpha = 0.05$ which is the threshold for rejecting $H_0$. 4. **Observed data:** The test statistic observed is $x = 26$ successes out of $n = 30$ trials. 5. **Null distribution:** Under $H_0$, $X \sim B(30, 0.7)$, a binomial distribution with $n=30$ and $p=0.7$. 6. **Calculate p-value:** Since $H_1$ is one-sided ($p > 0.7$), we calculate the upper tail probability: $$\text{p-value} = P(X \geq 26) \approx 0.0302$$ 7. **Decision rule:** Compare p-value to $\alpha$: - $0.0302 < 0.05$ so we reject $H_0$. 8. **Conclusion:** There is sufficient evidence at the 5% significance level to conclude the new method has a higher success rate than the traditional method. This means the claim that the new method is better is justified based on the data.