Does It Pay to Plead Guilty? The accompanying table summarizes randomly selected sample data for San Francisco defendants in burglary cases (based on data from “Does It Pay to Plead Guilty? Differential Sentencing and the Functioning of the Criminal Courts,” by Brereton and Casper, Law and Society Review, Vol. 16, No. 1). All of the subjects had prior prison sentences. Use a 0.05 significance level to test the claim that the sentence (sent to prison or not sent to prison) is independent of the plea. If you were an attorney defending a guilty defendant, would these results suggest that you should encourage a guilty plea?


Guilty Plea

Not Guilty Plea

Sent to Prison

392

58

Not Sent to Prison

564

14

Short Answer

Expert verified

There is enough evidence to conclude that the prison sentence and the plea are not independent of each other.

Yes, these results encourage pleas for guilty defendants

Step by step solution

01

Given information

Data are given on the number of subjects who got sentenced and who did not, depending on whether they pleaded guilty or not.

02

Chi-square test for the independence of attributes

The chi-square test for independence of attributes is conducted to test the independence between the row variable (sentence) and the column variable (plea).

The null hypothesis is as follows:

\[{H_o}:\]The prison sentence is independent of the plea.

The alternative hypothesis is as follows:

\[{H_1}:\]The prison sentenceis not independent of the plea.

It is a right-tailed test

03

Observed and expected frequencies

Observed Frequency:

The frequencies provided in the table are the observed frequencies. They are denoted by\(O\).

The following contingency table shows the observed frequency for each cell:


Guilty plea

Noguilty plea

Sent to prison

\[{O_1}\]=392

\[{O_2}\]=58

Not sent to prison

\[{O_3}\]=564

\[{O_4}\]=14

Expected Frequency:

The formula for computing the expected frequency for each cell is shown below:

\(E = \frac{{\left( {row\;total} \right)\left( {column\;total} \right)}}{{\left( {grand\;total} \right)}}\)

The row total for the first row is computed below:

\(\begin{array}{c}Row\;Tota{l_1} = 392 + 58\\ = 450\end{array}\)

The row total for the second row is computed below:

\(\begin{array}{c}Row\;Tota{l_2} = 564 + 14\\ = 578\end{array}\)

The column total for the first column is computed below:

\(\begin{array}{c}Column\;Tota{l_1} = 392 + 564\\ = 956\end{array}\)

The column total for the secondcolumn is computed below:

\(\begin{array}{c}Column\;Tota{l_2} = 58 + 14\\ = 72\end{array}\)

The grand total is computed as follows:

\(\begin{array}{c}Grand\;Total = \left( {450 + 578} \right)\\ = \left( {956 + 72} \right)\\ = 1028\end{array}\)

Thus, the following table shows the expected frequencies for each of the corresponding observed frequencies:


Guilty plea

Noguilty plea

Sent to prison

\[\begin{array}{c}{E_1} = \frac{{\left( {450} \right)\left( {956} \right)}}{{1028}}\\ = 418.482\end{array}\]

\[\begin{array}{c}{E_2} = \frac{{\left( {450} \right)\left( {72} \right)}}{{1028}}\\ = 31.518\end{array}\]

Not sent to prison

\[\begin{array}{c}{E_3} = \frac{{\left( {578} \right)\left( {956} \right)}}{{1028}}\\ = 537.518\end{array}\]

\[\begin{array}{c}{E_4} = \frac{{\left( {578} \right)\left( {72} \right)}}{{1028}}\\ = 40.482\end{array}\]

04

Test statistic

The test statistic is computed below:

\[\begin{array}{c}{\chi ^2} = \sum {\frac{{{{\left( {O - E} \right)}^2}}}{E}\;\;\; \sim } {\chi ^2}_{\left( {r - 1} \right)\left( {c - 1} \right)}\\ = \frac{{{{\left( {392 - 418.482} \right)}^2}}}{{418.482}} + \frac{{{{\left( {58 - 31.518} \right)}^2}}}{{31.518}} + \frac{{{{\left( {564 - 537.518} \right)}^2}}}{{537.518}} + \frac{{{{\left( {14 - 40.482} \right)}^2}}}{{40.482}}\\ = 42.557\end{array}\]

Thus,\({\chi ^2} = 42.557\).

Let r denote the number of rows in the contingency table.

Let cdenote the number of columns in the contingency table.

The degrees of freedom are computed below:

\(\begin{array}{c}df = \left( {r - 1} \right)\left( {c - 1} \right)\\ = \left( {2 - 1} \right)\left( {2 - 1} \right)\\ = 1\end{array}\)

The critical value of\({\chi ^2}\)for 1 degree of freedom at 0.05 level of significance for a right-tailed test is equal to 3.8415.

The corresponding p-value is approximately equal to 0.000.

Since the value of the test statistic is greater than the critical value and the p-value is less than 0.05,the null hypothesis is rejected.

05

Conclusion

There is enough evidence to conclude that the prison sentence and the plea are not independent of each other.

Since the number of guilty defendants who are not sent to prison if they plead guilty than those who do not is substantially larger, the results encourage the guilty defendants to plead guilty.

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Most popular questions from this chapter

Interaction

a. What is an interaction between two factors?

b. In general, when using two-way analysis of variance, if we find that there is an interaction effect, how does that affect the procedure?

c. Shown below is an interaction graph constructed from the data in Exercise 1. What does the graph suggest?

In Exercises 5–16, use analysis of variance for the indicated test.

Triathlon Times Jeff Parent is a statistics instructor who participates in triathlons. Listed below are times (in minutes and seconds) he recorded while riding a bicycle for five stages through each mile of a 3-mile loop. Use a 0.05 significance level to test the claim that it takes the same time to ride each of the miles. Does one of the miles appear to have a hill?

Mile 1

3:15

3:24

3:23

3:22

3:21

Mile 2

3:19

3:22

3:21

3:17

3:19

Mile 3

3:34

3:31

3:29

3:31

3:29

Tukey Test

A display of the Bonferroni test results from Table 12-1 (which is part ofthe Chapter Problem) is provided on page 577. Shown on the top of the next page is the SPSS-generated display of results from the Tukey test using the same data. Compare the Tukey test results to those from the Bonferroni test.

Cola Weights The displayed results from Exercise 1 are from one-way analysis of variance. What is it about this test that characterizes it as one-way analysis of variance instead of two-way analysis of variance?

In Exercises 1–4, use the following listed arrival delay times (minutes) for American Airline flights from New York to Los Angeles. Negative values correspond to flights that arrived early. Also shown are the SPSS results for analysis of variance. Assume that we plan to use a 0.05 significance level to test the claim that the different flights have the same mean arrival delay time.

Flight 1

-32

-25

-26

-6

5

-15

-17

-36

Flight 19

-5

-32

-13

-9

-19

49

-30

-23

Flight 21

-23

28

103

-19

-5

-46

13

-3

ANOVA

a. What characteristic of the data above indicates that we should use one-way analysis of variance?

b. If the objective is to test the claim that the three flights have the same mean arrival delay time, why is the method referred to as analysis of variance?

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