To determine whether to reject the null hypothesis $H_0$, we compare the computed p-value with the significance level $\alpha$.

Given:

• $\alpha = 0.05$
• Computed p-value = 0.022

Decision Rule:

If the p-value is less than $\alpha$, we reject the null hypothesis $H_0$. If the p-value is greater than or equal to $\alpha$, we fail to reject $H_0$.

Analysis:

Since the computed p-value (0.022) is less than the significance level $\alpha$ (0.05), we reject the null hypothesis $H_0$.

Conclusion:

The correct conclusion is:

• "There IS sufficient evidence to reject the Null Hypothesis and accept the alternative."

Would you like further details on hypothesis testing or have any questions?

Related Questions:

1. What does a p-value represent in hypothesis testing?
2. How does changing the significance level affect the decision to reject $H_0$?
3. What are Type I and Type II errors in hypothesis testing?
4. Can we determine the effect size from this information?
5. How would the conclusion change if the p-value were exactly 0.05?

Tip:

Always compare the p-value directly to the significance level to make a clear decision on rejecting or failing to reject the null hypothesis.