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Table of Critical Values of t

How is the table of critical values of t used to find the correct critical value \(t^*\) for confidence intervals and hypothesis tests?

Subject: Statistics Chapter: Estimation of the Mean and Proportion Topic: Estimation of a Population Mean σ Not Known the T Distribution Answer included
table critical values of t t table critical t value Student t distribution degrees of freedom t alpha t alpha over 2 confidence interval
Accepted answer Answer included

Table Critical Values of t

The table of critical values of t (often called a t-table) provides cutoffs \(t_{\alpha,df}\) for the Student t distribution. These cutoffs are used as \(t^*\) to build confidence intervals and to define rejection regions in hypothesis tests when the population standard deviation \(\sigma\) is unknown.

Step 1: Identify Degrees of Freedom

For one-sample inference about a population mean using a sample standard deviation \(s\), the degrees of freedom are:

\[ df = n - 1. \]

The t distribution depends on \(df\): smaller \(df\) produces heavier tails, so critical values are larger in magnitude.

Step 2: Decide Which Tail Probability the Table Uses

Many t-tables label columns by a right-tail area \(\alpha\), giving \(t_{\alpha,df}\) such that \(P(T \ge t_{\alpha,df})=\alpha\). Some tables label columns by a two-tail area \(2\alpha\). The correct column depends on whether the procedure is one-sided or two-sided.

For a two-sided confidence interval with confidence level \(C\), the total tail area is \(\alpha=1-C\), so each tail has area \(\alpha/2\). The needed critical value is \(t_{\alpha/2,df}\).

Step 3: Use the Correct Critical Value

Goal Probability statement Critical value to read from the t-table
Two-sided confidence interval at level \(C\) \(P(-t^* \le T \le t^*)=C\) \(t^* = t_{\alpha/2,df}\) where \(\alpha = 1-C\)
Two-sided hypothesis test at significance \(\alpha\) Reject if \(|t| \ge t^*\) \(t^* = t_{\alpha/2,df}\)
Right-tailed hypothesis test at significance \(\alpha\) Reject if \(t \ge t^*\) \(t^* = t_{\alpha,df}\)
Left-tailed hypothesis test at significance \(\alpha\) Reject if \(t \le -t^*\) \(t^* = t_{\alpha,df}\) and use \(-t^*\)

How the Critical t Value Enters a Confidence Interval

For a one-sample confidence interval for a population mean (with \(\sigma\) unknown), the margin of error uses \(t^*\):

\[ \bar{x} \pm t^* \cdot \frac{s}{\sqrt{n}}, \qquad \text{with } t^* = t_{\alpha/2,df},\; df=n-1. \]

Worked Example Using a t-Table

A sample of size \(n=12\) is used to estimate a population mean with a 95% confidence interval. Here \(C=0.95\), so \(\alpha=1-0.95=0.05\) and \(\alpha/2=0.025\). Degrees of freedom:

\[ df = n-1 = 12-1 = 11. \]

From the table of critical values of t, locate the row \(df=11\) and the column for right-tail area \(0.025\) (equivalently, a two-tail area of \(0.05\)). The critical value is:

\[ t^* = t_{0.025,11} \approx 2.201. \]

This \(t^*\) is then substituted into the margin of error \(t^*\cdot \dfrac{s}{\sqrt{n}}\).

Visualization: Two-Tail Critical Regions for \(t_{\alpha/2,df}\)

-t* 0 t* two-tail: each shaded tail is \(\alpha/2\) center area is \(1-\alpha\), critical values are \(\pm t_{\alpha/2,df}\)
A two-sided procedure splits the total significance level \(\alpha\) into two equal tails. The table of critical values of t supplies \(t_{\alpha/2,df}\); the cutoffs are \(\pm t_{\alpha/2,df}\).

Quick Checklist Before Reading a t-Table

  1. Compute \(df\) (often \(n-1\) for one-sample mean inference).
  2. Determine whether the problem is two-sided (\(\alpha/2\) per tail) or one-sided (\(\alpha\) in one tail).
  3. Match the table’s column definition (right-tail \(\alpha\) vs two-tail \(\alpha\)) to the problem.
  4. Use symmetry: left-tail critical values are the negatives of right-tail values.

A common two-sided mistake is using \(t_{\alpha,df}\) instead of \(t_{\alpha/2,df}\). For confidence level \(C\) or two-sided significance \(\alpha\), the correct column uses \(\alpha/2\).

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