t Distribution

The t-distribution, also known as Student’s t-distribution, is a continuous probability distribution that is used in statistical inference when the sample size is small and the population standard deviation is unknown. It is a bell-shaped, symmetric distribution that is similar to the normal distribution, but with heavier tails.

Degrees of freedom

The degrees of freedom for the t-distribution is the number of observations in the sample minus one. This is because the sample mean and sample standard deviation are used to estimate the population mean and population standard deviation, and these estimates lose one degree of freedom each.

Example

Suppose we have a sample of 10 observations from a population with unknown mean and standard deviation. The sample mean is 50 and the sample standard deviation is 10. We want to test the hypothesis that the population mean is equal to 55.

To do this, we use a t-test. The degrees of freedom for the t-test is 9 (10 observations minus one). The critical value for a two-tailed test with a significance level of 0.05 is 2.262.

The t-statistic is calculated as follows:

$$t = \frac{\overline{x} - \mu_0}{s/\sqrt{n}}$$

where:

• $\overline{x}$ is the sample mean
• $\mu_0$ is the hypothesized population mean
• $s$ is the sample standard deviation
• $n$ is the sample size

In this case, the t-statistic is:

$$t = \frac{50 - 55}{10/\sqrt{10}} = -1.732$$

The p-value for the t-test is calculated as follows:

$$p-value = 2P(t < -1.732) = 0.114$$

Since the p-value is greater than the significance level of 0.05, we fail to reject the hypothesis that the population mean is equal to 55.

The t-distribution is a useful tool for statistical inference when the sample size is small and the population standard deviation is unknown. It is a bell-shaped, symmetric distribution that is similar to the normal distribution, but with heavier tails. The t-distribution is used in a variety of statistical applications, including hypothesis testing about the mean of a population, confidence intervals for the mean of a population, paired t-tests for comparing the means of two related samples, and independent t-tests for comparing the means of two independent samples.

t distribution formula

The t-distribution is a continuous probability distribution that is used to estimate the mean of a normally distributed population when the sample size is small. It is also used to test hypotheses about the mean of a normally distributed population.

The t-distribution is defined by the following formula:

$$t = \frac{\bar{X} - \mu}{s/\sqrt{n}}$$

where:

• $t$ is the t-statistic
• $\bar{X}$ is the sample mean
• $\mu$ is the population mean
• $s$ is the sample standard deviation
• $n$ is the sample size

The t-distribution has a number of properties that make it useful for statistical inference. These properties include:

• The t-distribution is symmetric about zero.
• The t-distribution has a mean of zero.
• The t-distribution has a variance of $\frac{n}{n-2}$.
• The t-distribution has a kurtosis of $\frac{6}{n-4}$.
• The t-distribution approaches the standard normal distribution as the sample size increases.

T Distribution Table

The t-distribution table provides the critical values of the t-distribution for various degrees of freedom and levels of significance. These critical values can be used to determine the p-value for a t-test or to construct a confidence interval for the mean of a normally distributed population.

The following table provides the critical values of the t-distribution for selected degrees of freedom and levels of significance:

The t-distribution is a useful tool for statistical inference. It is used to test hypotheses about the mean of a normally distributed population, to construct confidence intervals for the mean of a normally distributed population, and to test the significance of regression coefficients.

How to Use the t Distribution Table

To use the t-distribution table, you need to know the following information:

• The degrees of freedom (df) for the t-statistic. The degrees of freedom are equal to the sample size minus one.
• The significance level (α) for the test. The significance level is the probability of rejecting the null hypothesis when it is actually true.

Once you have this information, you can find the critical value for the t-statistic by looking up the corresponding value in the t-distribution table. The critical value is the value of the t-statistic that corresponds to the given degrees of freedom and significance level.

Example

Suppose you have a sample of 10 observations from a population with an unknown mean. You want to test the hypothesis that the population mean is equal to 50. The sample mean is 55 and the sample standard deviation is 10.

To perform this test, you would first calculate the t-statistic:

$$t = \frac{\overline{x} - \mu_0}{s/\sqrt{n}}$$

where:

• $\overline{x}$ is the sample mean
• $\mu_0$ is the hypothesized population mean
• $s$ is the sample standard deviation
• $n$ is the sample size

In this case, the t-statistic is:

$$t = \frac{55 - 50}{10/\sqrt{10}} = 1.732$$

Next, you would look up the critical value for the t-statistic in the t-distribution table. The degrees of freedom for this test are 9 (since the sample size is 10), and the significance level is 0.05. The critical value is 2.262.

Since the absolute value of the t-statistic (1.732) is less than the critical value (2.262), we fail to reject the null hypothesis. This means that there is not enough evidence to conclude that the population mean is different from 50.

The t-distribution table is a valuable tool for performing hypothesis tests about the mean of a population when the sample size is small and the population standard deviation is unknown. By using the t-distribution table, you can determine the critical value for the t-statistic and make a decision about whether to reject or fail to reject the null hypothesis.

Parameter of t distribution

The t-distribution is a continuous probability distribution that is used to estimate the mean of a normally distributed population when the sample size is small. It is also known as Student’s t-distribution, after the pseudonym of the statistician who first described it, William Sealy Gosset.

The t-distribution has one parameter, which is the degrees of freedom. The degrees of freedom are equal to the sample size minus one.

The degrees of freedom

The degrees of freedom are a measure of the amount of information in a sample. The more degrees of freedom, the more information there is in the sample, and the more precise the estimate of the population mean will be.

The degrees of freedom affect the shape of the t-distribution. The t-distribution with a small number of degrees of freedom is more spread out than the t-distribution with a large number of degrees of freedom. This means that the t-distribution with a small number of degrees of freedom is more likely to produce extreme values.

The t-distribution is a powerful tool for statistical inference. It is used in a variety of applications, and it is important to understand the parameters of the t-distribution in order to use it correctly.

Difference between t distribution and Normal distribution

The t-distribution and the normal distribution are both continuous probability distributions that are used in statistical analysis. However, there are some key differences between the two distributions.

1. Shape

The normal distribution is a symmetric, bell-shaped curve. The t-distribution is also a bell-shaped curve, but it is not symmetric. The t-distribution has a heavier tail than the normal distribution, which means that it is more likely to produce extreme values.

2. Degrees of freedom

The t-distribution has a parameter called the degrees of freedom. The degrees of freedom determine the shape of the distribution. The more degrees of freedom, the more the t-distribution approaches the normal distribution.

4. Which distribution to use?

The choice of which distribution to use depends on the specific statistical analysis that is being performed. If the sample size is large, the normal distribution can be used. If the sample size is small, the t-distribution should be used.

5. Summary

The t-distribution and the normal distribution are both important continuous probability distributions that are used in statistical analysis. The key differences between the two distributions are:

• The t-distribution is not symmetric, while the normal distribution is.
• The t-distribution has a parameter called the degrees of freedom, which determines the shape of the distribution.
• The t-distribution is used in a more limited number of applications than the normal distribution.

The choice of which distribution to use depends on the specific statistical analysis that is being performed.

Properties of t distribution

The t-distribution is a continuous probability distribution that is similar to the normal distribution, but it has heavier tails. This means that the t-distribution is more likely to produce extreme values than the normal distribution.

The t-distribution is defined by one parameter, the degrees of freedom. The degrees of freedom determine the shape of the distribution. The larger the degrees of freedom, the more the t-distribution resembles the normal distribution.

The t-distribution has a number of properties that make it useful for statistical inference. These properties include:

• Symmetry: The t-distribution is symmetric about its mean.
• Unimodality: The t-distribution is unimodal, meaning that it has a single mode.
• Heavy tails: The t-distribution has heavier tails than the normal distribution. This means that the t-distribution is more likely to produce extreme values than the normal distribution.
• Skewness: The t-distribution is skewed to the right for small degrees of freedom. As the degrees of freedom increase, the skewness decreases.
• Kurtosis: The t-distribution is more kurtosis than the normal distribution. This means that the t-distribution has a higher peak and thicker tails than the normal distribution.

Applications of the t-distribution

The t-distribution is used in a variety of statistical applications, including:

• Hypothesis testing: The t-distribution is used to test hypotheses about the mean of a population when the sample size is small and the population standard deviation is unknown.
• Confidence intervals: The t-distribution is used to construct confidence intervals for the mean of a population when the sample size is small and the population standard deviation is unknown.
• Regression analysis: The t-distribution is used to test hypotheses about the slope and intercept of a regression line.
• Analysis of variance: The t-distribution is used to test hypotheses about the equality of means between two or more groups.

The t-distribution is a powerful tool for statistical inference. It is important to understand the properties of the t-distribution in order to use it correctly.

Application of Student’s t-distribution

The Student’s t-distribution is a probability distribution that is used in statistical inference when the sample size is small and the population standard deviation is unknown. It is a bell-shaped, symmetric distribution that is similar to the normal distribution, but it has thicker tails. This means that the t-distribution is more likely to produce extreme values than the normal distribution.

The t-distribution is used in a variety of statistical applications, including:

• Hypothesis testing: The t-distribution can be used to test hypotheses about the mean of a population when the sample size is small and the population standard deviation is unknown. For example, a researcher might use a t-test to test the hypothesis that the mean weight of women in the United States is 150 pounds.
• Confidence intervals: The t-distribution can be used to construct confidence intervals for the mean of a population when the sample size is small and the population standard deviation is unknown. For example, a researcher might use a t-interval to estimate the mean weight of women in the United States with 95% confidence.
• Regression analysis: The t-distribution can be used to test the significance of regression coefficients in a regression model. For example, a researcher might use a t-test to test the hypothesis that the slope of a regression line is equal to zero.

Advantages of the t-distribution

The t-distribution has several advantages over the normal distribution when the sample size is small and the population standard deviation is unknown. These advantages include:

• Robustness: The t-distribution is more robust to violations of the normality assumption than the normal distribution. This means that the t-distribution can be used even when the population is not normally distributed.
• Power: The t-distribution is more powerful than the normal distribution when the sample size is small. This means that the t-distribution is more likely to detect a difference between the sample mean and the population mean when the difference is small.

Disadvantages of the t-distribution

The t-distribution also has some disadvantages, including:

• Complexity: The t-distribution is more complex than the normal distribution. This means that it can be more difficult to understand and use.
• Accuracy: The t-distribution is less accurate than the normal distribution when the sample size is large. This means that the t-distribution is more likely to produce incorrect results when the sample size is large.

Overall, the t-distribution is a valuable tool for statistical inference when the sample size is small and the population standard deviation is unknown. It is more robust and powerful than the normal distribution, but it is also more complex and less accurate.

t Distribution FAQs

What is the t-distribution?

The t-distribution is a continuous probability distribution that is used to estimate the mean of a normally distributed population when the sample size is small and the population standard deviation is unknown. It is also used to test hypotheses about the mean of a normally distributed population.

What are the key characteristics of the t-distribution?

The t-distribution is a symmetric, bell-shaped distribution that is centered at zero. The spread of the t-distribution is determined by the degrees of freedom, which is a measure of the sample size. The t-distribution has thicker tails than the normal distribution, which means that it is more likely to produce extreme values.

What are the applications of the t-distribution?

The t-distribution is used in a variety of statistical applications, including:

• Estimating the mean of a normally distributed population when the sample size is small and the population standard deviation is unknown.
• Testing hypotheses about the mean of a normally distributed population.
• Constructing confidence intervals for the mean of a normally distributed population.
• Performing paired t-tests to compare the means of two related samples.
• Performing independent t-tests to compare the means of two independent samples.

What are the limitations of the t-distribution?

The t-distribution is a powerful tool for statistical analysis, but it does have some limitations. These limitations include:

• The t-distribution is only valid for normally distributed populations.
• The t-distribution is sensitive to outliers.
• The t-distribution is not as powerful as the normal distribution when the sample size is large.

Conclusion

The t-distribution is a versatile and powerful tool for statistical analysis. It is important to understand the key characteristics and limitations of the t-distribution in order to use it effectively.