Critical Values for the Standard Normal Distribution

Oct 13, 2025 by sorusgentrification.com 136 views

Introduction

In hypothesis testing, we often encounter scenarios where we need to determine whether a given sample statistic is significantly different from a known population parameter. In this article, we will explore a test of the null hypothesis $H_0: p=0.4$ versus the alternative hypothesis $H_a: p > 0.4$ with a test statistic $z = 2.52$ . We will examine the conclusions that can be drawn at the 5% and 1% significance levels.

Part A: Conclusion at the 5% and 1% Significance Levels

To determine the conclusion at the 5% and 1% significance levels, we need to compare the test statistic $z = 2.52$ with the critical values from the standard normal distribution.

Critical Values for the Standard Normal Distribution

The critical values for the standard normal distribution at the 5% and 1% significance levels are:

5% significance level: $z_{0.05} = 1.645$
1% significance level: $z_{0.01} = 2.326$

Comparison with Critical Values

Now, let's compare the test statistic $z = 2.52$ with the critical values:

At the 5% significance level: $z = 2.52 > z_{0.05} = 1.645$ . Since the test statistic is greater than the critical value, we reject the null hypothesis $H_0: p=0.4$ .
At the 1% significance level: $z = 2.52 > z_{0.01} = 2.326$ . Since the test statistic is greater than the critical value, we reject the null hypothesis $H_0: p=0.4$ .

Conclusion

Based on the comparison with the critical values, we can conclude that:

At the 5% significance level, we reject the null hypothesis $H_0: p=0.4$ in favor of the alternative hypothesis $H_a: p > 0.4$ .
At the 1% significance level, we reject the null hypothesis $H_0: p=0.4$ in favor of the alternative hypothesis $H_a: p > 0.4$ .

Part B: Implications of the Test

The rejection of the null hypothesis $H_0: p=0.4$ in favor of the alternative hypothesis $H_a: p > 0.4$ implies that the true population proportion $p$ is likely to be greater than 0.4. This has important implications for the population being studied, as it suggests that the proportion of individuals with a certain characteristic is higher than previously thought.

Discussion

The test of $H_0: p=0.4$ versus $H_a: p > 0.4$ with a test statistic $z = 2.52$ provides strong evidence against the null hypothesis at both the 5% and 1% significance levels. The rejection of the null hypothesis has important implications for the population being studied, as it suggests that the proportion of individuals with a certain characteristic is higher than previously thought.

Limitations of the Test

While the test provides strong evidence against the null hypothesis, it is essential to consider the limitations of the test. The test assumes that the sample is randomly selected from the population and that the sample size is sufficiently large to ensure that the sampling distribution of the test statistic is approximately normal. Additionally, the test assumes that the population proportion $p$ is constant across all subgroups of the population.

Conclusion

In conclusion, the test of $H_0: p=0.4$ versus $H_a: p > 0.4$ with a test statistic $z = 2.52$ provides strong evidence against the null hypothesis at both the 5% and 1% significance levels. The rejection of the null hypothesis has important implications for the population being studied, as it suggests that the proportion of individuals with a certain characteristic is higher than previously thought. However, it is essential to consider the limitations of the test and to ensure that the assumptions of the test are met.

References

[1] Moore, D. S., & McCabe, G. P. (2013). Introduction to the practice of statistics. W.H. Freeman and Company.
[2] Rosner, B. (2010). Fundamentals of biostatistics. Cengage Learning.
[3] Zar, J. H. (2010). Biostatistical analysis. Prentice Hall.

Keywords

Hypothesis testing
Standard normal distribution
Critical values
Significance levels
Null hypothesis
Alternative hypothesis
Test statistic
Population proportion
Sampling distribution
Random sampling
Sample size

Introduction

In our previous article, we explored a test of the null hypothesis $H_0: p=0.4$ versus the alternative hypothesis $H_a: p > 0.4$ with a test statistic $z = 2.52$ . We examined the conclusions that can be drawn at the 5% and 1% significance levels. In this article, we will provide a Q&A section to address common questions and concerns related to the test.

Q&A

Q1: What is the purpose of hypothesis testing?

A1: The purpose of hypothesis testing is to determine whether a given sample statistic is significantly different from a known population parameter. In this case, we are testing whether the true population proportion $p$ is greater than 0.4.

Q2: What is the significance level?

A2: The significance level is the maximum probability of rejecting the null hypothesis when it is true. In this case, we are using a 5% and 1% significance level.

Q3: What is the test statistic?

A3: The test statistic is a numerical value that is used to determine whether the null hypothesis should be rejected. In this case, the test statistic is $z = 2.52$ .

Q4: How do we determine the critical values?

A4: The critical values are determined using the standard normal distribution. We use the critical values to determine whether the test statistic is significantly different from the null hypothesis.

Q5: What is the difference between the 5% and 1% significance levels?

A5: The 5% significance level is more conservative than the 1% significance level. This means that we are more likely to reject the null hypothesis at the 5% significance level than at the 1% significance level.

Q6: What are the implications of rejecting the null hypothesis?

A6: Rejecting the null hypothesis means that we have evidence that the true population proportion $p$ is greater than 0.4. This has important implications for the population being studied.

Q7: What are the limitations of the test?

A7: The test assumes that the sample is randomly selected from the population and that the sample size is sufficiently large to ensure that the sampling distribution of the test statistic is approximately normal. Additionally, the test assumes that the population proportion $p$ is constant across all subgroups of the population.

Q8: How do we interpret the results of the test?

A8: We interpret the results of the test by comparing the test statistic with the critical values. If the test statistic is greater than the critical value, we reject the null hypothesis.

Conclusion

References

[1] Moore, D. S., & McCabe, G. P. (2013). Introduction to the practice of statistics. W.H. Freeman and Company.
[2] Rosner, B. (2010). Fundamentals of biostatistics. Cengage Learning.
[3] Zar, J. H. (2010). Biostatistical analysis. Prentice Hall.

Keywords

Hypothesis testing
Standard normal distribution
Critical values
Significance levels
Null hypothesis
Alternative hypothesis
Test statistic
Population proportion
Sampling distribution
Random sampling
Sample size