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one sample t and z test

# One-Sample t-Test and z-Test

Module - 7 Hypothesis Testing
One-Sample t-Test and z-Test

Overview

In statistics, hypothesis testing is a common method used to determine if there is a significant difference between a sample and a known population. One of the most commonly used tests in hypothesis testing is the one-sample t-test and z-test. In this lesson, we will explore the differences between the two tests and how they are used in practice.

One-sample t-test

The one-sample t-test is a statistical test used to determine if a sample mean is significantly different from a known population mean. It is used when the population standard deviation is unknown and must be estimated from the sample data. The formula for the one-sample t-test is:

t = (x̄ - μ) / (s / √n)


where:

• t = the t-statistic
• x̄ = the sample mean
• μ = the known population mean
• s = the sample standard deviation
• n = the sample size

The t-statistic is used to calculate the p-value, which is the probability of obtaining a sample mean as extreme as the one observed, assuming the null hypothesis is true. If the p-value is less than the significance level (typically 0.05), we reject the null hypothesis and conclude that there is a significant difference between the sample mean and the population mean.

Example 1:

Suppose a company wants to know if the average time spent on their website per session is significantly different from the industry average of 5 minutes. They collect a random sample of 50 website sessions and find that the average time spent is 4.5 minutes with a standard deviation of 1.2 minutes. They can use the one-sample t-test to test the hypothesis that the company's website time is equal to the industry average.

The null hypothesis would be: H0: μ = 5 The alternative hypothesis would be: Ha: μ ≠ 5

Using the formula for the one-sample t-test, we get:

t = (4.5 - 5) / (1.2 / √50) = -2.08


With 49 degrees of freedom (n-1), and a significance level of 0.05, we find the critical t-value to be ±2.009. Since our calculated t-value (-2.08) falls outside the critical region, we reject the null hypothesis and conclude that the average time spent on the company's website is significantly different from the industry average.

Z-test

The z-test is a statistical test used to determine if a sample mean is significantly different from a known population mean. It is used when the population standard deviation is known. The formula for the z-test is:

z = (x̄ - μ) / (σ / √n)


where:

• z = the z-statistic
• x̄ = the sample mean
• μ = the known population mean
• σ = the population standard deviation
• n = the sample size

The z-statistic is used to calculate the p-value, which is the probability of obtaining a sample mean as extreme as the one observed, assuming the null hypothesis is true. If the p-value is less than the significance level (typically 0.05), we reject the null hypothesis and conclude that there is a significant difference between the sample mean and the population mean. Example 2:

Suppose a company wants to know if the average weight of their product is significantly different from the target weight of 500 grams. They collect a random sample of 100 products and find that the average weight is 498 grams with a standard deviation of 10 grams. They know from previous production runs that the population standard deviation is 5 grams. They can use the z-test to test the hypothesis that the product weight is equal to the target weight.

The null hypothesis would be: H0: μ = 500 The alternative hypothesis would be: Ha: μ ≠ 500

Using the formula for the z-test, we get:

z = (498 - 500) / (5 / √100) = -4


Difference between the one-sample t-test and z-test

The main difference between the one-sample t-test and z-test is in the assumptions made about the population standard deviation. The one-sample t-test assumes that the population standard deviation is unknown and must be estimated from the sample data, while the z-test assumes that the population standard deviation is known.

Another difference is in the distribution used to calculate the p-value. The one-sample t-test uses the t-distribution, which has heavier tails than the normal distribution used in the z-test. This means that the t-test is more conservative and requires a larger sample size to achieve the same level of power as the z-test. Conclusion

The one-sample t-test and z-test are both useful statistical tests for hypothesis testing. The choice between the two tests depends on whether the population standard deviation is known or unknown. The one-sample t-test is more commonly used because the population standard deviation is often unknown. However, if the population standard deviation is known, the z-test is a more powerful test.

Key takeaways

Here are some key takeaways from this lesson:

1. Hypothesis testing is a common method used to determine if there is a significant difference between a sample and a known population.
2. The one-sample t-test is used when the population standard deviation is unknown and must be estimated from the sample data, while the z-test is used when the population standard deviation is known.
3. Both tests use the sample mean, population mean, sample size, and a calculated statistic (t or z) to determine the p-value and make conclusions about the null hypothesis.
4. The t-test is more conservative and requires a larger sample size to achieve the same level of power as the z-test because it uses the t-distribution, which has heavier tails than the normal distribution used in the z-test.
5. The choice between the two tests depends on whether the population standard deviation is known or unknown. The one-sample t-test is more commonly used because the population standard deviation is often unknown.

Quiz

1. What is the one-sample t-test used for?

1. To determine if a sample mean is significantly different from a known population mean when the population standard deviation is unknown.
2. To determine if a sample mean is significantly different from a known population mean when the population standard deviation is known.
3. To determine if a sample proportion is significantly different from a known population proportion.
4. To determine if two samples are significantly different from each other.

Answer: a. To determine if a sample mean is significantly different from a known population mean when the population standard deviation is unknown.

2. What is the z-test used for?

1. To determine if a sample mean is significantly different from a known population mean when the population standard deviation is unknown.
2. To determine if a sample mean is significantly different from a known population mean when the population standard deviation is known.
3. To determine if a sample proportion is significantly different from a known population proportion.
4. To determine if two samples are significantly different from each other.

Answer: b. To determine if a sample mean is significantly different from a known population mean when the population standard deviation is known.

3. What is the main difference between the one-sample t-test and z-test?

1. The distribution used to calculate the p-value.
3. The type of data being tested.
4. The number of samples being compared.

4. Which test is more commonly used and why?

a. The one-sample t-test, because the population standard deviation is often unknown.

b. The z-test, because the population standard deviation is often unknown.

c. The one-sample t-test, because the population standard deviation is often known.

d. The z-test, because the population standard deviation is often known.

Answer: a. The one-sample t-test, because the population standard deviation is often unknown.

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