Fixed javadoc errors. One-sided t-test significance adjustment was
reversed in javadoc for boolean-valued test methods. BZ #34448 Reported by: Gilles Gaillard git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@161613 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Phil Steitz
parent
2eb28c766c
commit
4a4f843f04
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@ -110,7 +110,7 @@ public interface TTest {
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* <p>
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* Returns <code>true</code> iff the null hypothesis can be rejected with
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* confidence <code>1 - alpha</code>. To perform a 1-sided test, use
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* <code>alpha / 2</code>
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* <code>alpha * 2</code>
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* <p>
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* <strong>Usage Note:</strong><br>
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* The validity of the test depends on the assumptions of the parametric
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@ -347,7 +347,7 @@ public interface TTest {
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* <p>
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* Returns <code>true</code> iff the null hypothesis can be
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* rejected with confidence <code>1 - alpha</code>. To
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* perform a 1-sided test, use <code>alpha / 2</code>
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* perform a 1-sided test, use <code>alpha * 2</code>
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* <p>
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* <strong>Examples:</strong><br><ol>
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* <li>To test the (2-sided) hypothesis <code>sample mean = mu </code> at
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@ -356,7 +356,7 @@ public interface TTest {
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* <li>To test the (one-sided) hypothesis <code> sample mean < mu </code>
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* at the 99% level, first verify that the measured sample mean is less
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* than <code>mu</code> and then use
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* <br><code>tTest(mu, sample, 0.005) </code>
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* <br><code>tTest(mu, sample, 0.02) </code>
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* </li></ol>
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* <p>
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* <strong>Usage Note:</strong><br>
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@ -415,7 +415,7 @@ public interface TTest {
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* <p>
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* Returns <code>true</code> iff the null hypothesis can be rejected with
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* confidence <code>1 - alpha</code>. To perform a 1-sided test, use
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* <code>alpha / 2.</code>
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* <code>alpha * 2.</code>
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* <p>
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* <strong>Examples:</strong><br><ol>
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* <li>To test the (2-sided) hypothesis <code>sample mean = mu </code> at
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@ -424,7 +424,7 @@ public interface TTest {
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* <li>To test the (one-sided) hypothesis <code> sample mean < mu </code>
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* at the 99% level, first verify that the measured sample mean is less
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* than <code>mu</code> and then use
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* <br><code>tTest(mu, sampleStats, 0.005) </code>
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* <br><code>tTest(mu, sampleStats, 0.02) </code>
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* </li></ol>
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* <p>
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* <strong>Usage Note:</strong><br>
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@ -535,7 +535,7 @@ public interface TTest {
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* <p>
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* Returns <code>true</code> iff the null hypothesis that the means are
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* equal can be rejected with confidence <code>1 - alpha</code>. To
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* perform a 1-sided test, use <code>alpha / 2</code>
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* perform a 1-sided test, use <code>alpha * 2</code>
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* <p>
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* See {@link #t(double[], double[])} for the formula used to compute the
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* t-statistic. Degrees of freedom are approximated using the
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@ -549,9 +549,9 @@ public interface TTest {
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* <br><code>tTest(sample1, sample2, 0.05). </code>
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* </li>
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* <li>To test the (one-sided) hypothesis <code> mean 1 < mean 2 </code>,
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* first verify that the measured mean of <code>sample 1</code> is less
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* than the mean of <code>sample 2</code> and then use
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* <br><code>tTest(sample1, sample2, 0.005) </code>
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* at the 99% level, first verify that the measured mean of <code>sample 1</code>
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* is less than the mean of <code>sample 2</code> and then use
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* <br><code>tTest(sample1, sample2, 0.02) </code>
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* </li></ol>
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* <p>
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* <strong>Usage Note:</strong><br>
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@ -591,7 +591,7 @@ public interface TTest {
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* <p>
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* Returns <code>true</code> iff the null hypothesis that the means are
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* equal can be rejected with confidence <code>1 - alpha</code>. To
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* perform a 1-sided test, use <code>alpha / 2.</code> To perform the test
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* perform a 1-sided test, use <code>alpha * 2.</code> To perform the test
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* without the assumption of equal subpopulation variances, use
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* {@link #tTest(double[], double[], double)}.
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* <p>
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@ -607,7 +607,7 @@ public interface TTest {
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* at the 99% level, first verify that the measured mean of
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* <code>sample 1</code> is less than the mean of <code>sample 2</code>
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* and then use
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* <br><code>tTest(sample1, sample2, 0.005) </code>
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* <br><code>tTest(sample1, sample2, 0.02) </code>
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* </li></ol>
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* <p>
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* <strong>Usage Note:</strong><br>
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@ -723,7 +723,7 @@ public interface TTest {
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* <p>
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* Returns <code>true</code> iff the null hypothesis that the means are
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* equal can be rejected with confidence <code>1 - alpha</code>. To
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* perform a 1-sided test, use <code>alpha / 2</code>
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* perform a 1-sided test, use <code>alpha * 2</code>
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* <p>
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* See {@link #t(double[], double[])} for the formula used to compute the
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* t-statistic. Degrees of freedom are approximated using the
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@ -739,7 +739,7 @@ public interface TTest {
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* at the 99% level, first verify that the measured mean of
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* <code>sample 1</code> is less than the mean of <code>sample 2</code>
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* and then use
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* <br><code>tTest(sampleStats1, sampleStats2, 0.005) </code>
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* <br><code>tTest(sampleStats1, sampleStats2, 0.02) </code>
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* </li></ol>
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* <p>
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* <strong>Usage Note:</strong><br>
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@ -123,7 +123,7 @@ public class TTestImpl implements TTest {
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* <p>
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* Returns <code>true</code> iff the null hypothesis can be rejected with
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* confidence <code>1 - alpha</code>. To perform a 1-sided test, use
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* <code>alpha / 2</code>
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* <code>alpha * 2</code>
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* <p>
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* <strong>Usage Note:</strong><br>
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* The validity of the test depends on the assumptions of the parametric
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@ -420,7 +420,7 @@ public class TTestImpl implements TTest {
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* <p>
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* Returns <code>true</code> iff the null hypothesis can be
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* rejected with confidence <code>1 - alpha</code>. To
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* perform a 1-sided test, use <code>alpha / 2</code>
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* perform a 1-sided test, use <code>alpha * 2</code>
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* <p>
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* <strong>Examples:</strong><br><ol>
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* <li>To test the (2-sided) hypothesis <code>sample mean = mu </code> at
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@ -429,7 +429,7 @@ public class TTestImpl implements TTest {
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* <li>To test the (one-sided) hypothesis <code> sample mean < mu </code>
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* at the 99% level, first verify that the measured sample mean is less
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* than <code>mu</code> and then use
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* <br><code>tTest(mu, sample, 0.005) </code>
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* <br><code>tTest(mu, sample, 0.02) </code>
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* </li></ol>
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* <p>
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* <strong>Usage Note:</strong><br>
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@ -501,7 +501,7 @@ public class TTestImpl implements TTest {
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* <p>
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* Returns <code>true</code> iff the null hypothesis can be rejected with
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* confidence <code>1 - alpha</code>. To perform a 1-sided test, use
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* <code>alpha / 2.</code>
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* <code>alpha * 2.</code>
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* <p>
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* <strong>Examples:</strong><br><ol>
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* <li>To test the (2-sided) hypothesis <code>sample mean = mu </code> at
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@ -510,7 +510,7 @@ public class TTestImpl implements TTest {
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* <li>To test the (one-sided) hypothesis <code> sample mean < mu </code>
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* at the 99% level, first verify that the measured sample mean is less
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* than <code>mu</code> and then use
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* <br><code>tTest(mu, sampleStats, 0.005) </code>
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* <br><code>tTest(mu, sampleStats, 0.02) </code>
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* </li></ol>
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* <p>
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* <strong>Usage Note:</strong><br>
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@ -656,10 +656,10 @@ public class TTestImpl implements TTest {
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* the 95% level, use
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* <br><code>tTest(sample1, sample2, 0.05). </code>
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* </li>
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* <li>To test the (one-sided) hypothesis <code> mean 1 < mean 2 </code>,
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* first verify that the measured mean of <code>sample 1</code> is less
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* than the mean of <code>sample 2</code> and then use
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* <br><code>tTest(sample1, sample2, 0.005) </code>
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* <li>To test the (one-sided) hypothesis <code> mean 1 < mean 2 </code> at
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* the 99% level, first verify that the measured mean of <code>sample 1</code>
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* is less than the mean of <code>sample 2</code> and then use
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* <br><code>tTest(sample1, sample2, 0.02) </code>
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* </li></ol>
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* <p>
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* <strong>Usage Note:</strong><br>
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@ -703,7 +703,7 @@ public class TTestImpl implements TTest {
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* <p>
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* Returns <code>true</code> iff the null hypothesis that the means are
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* equal can be rejected with confidence <code>1 - alpha</code>. To
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* perform a 1-sided test, use <code>alpha / 2.</code> To perform the test
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* perform a 1-sided test, use <code>alpha * 2.</code> To perform the test
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* without the assumption of equal subpopulation variances, use
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* {@link #tTest(double[], double[], double)}.
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* <p>
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@ -719,7 +719,7 @@ public class TTestImpl implements TTest {
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* at the 99% level, first verify that the measured mean of
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* <code>sample 1</code> is less than the mean of <code>sample 2</code>
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* and then use
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* <br><code>tTest(sample1, sample2, 0.005) </code>
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* <br><code>tTest(sample1, sample2, 0.02) </code>
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* </li></ol>
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* <p>
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* <strong>Usage Note:</strong><br>
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@ -855,7 +855,7 @@ public class TTestImpl implements TTest {
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* <p>
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* Returns <code>true</code> iff the null hypothesis that the means are
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* equal can be rejected with confidence <code>1 - alpha</code>. To
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* perform a 1-sided test, use <code>alpha / 2</code>
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* perform a 1-sided test, use <code>alpha * 2</code>
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* <p>
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* See {@link #t(double[], double[])} for the formula used to compute the
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* t-statistic. Degrees of freedom are approximated using the
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@ -871,7 +871,7 @@ public class TTestImpl implements TTest {
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* at the 99% level, first verify that the measured mean of
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* <code>sample 1</code> is less than the mean of <code>sample 2</code>
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* and then use
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* <br><code>tTest(sampleStats1, sampleStats2, 0.005) </code>
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* <br><code>tTest(sampleStats1, sampleStats2, 0.02) </code>
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* </li></ol>
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* <p>
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* <strong>Usage Note:</strong><br>
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@ -500,7 +500,7 @@ testStatistic.pairedT(sample1, sample2);
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</source>
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</p>
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<p>
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To compute the (one-sided) p-value:
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To compute the p-value:
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<source>
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testStatistic.pairedTTest(sample1, sample2);
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</source>
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@ -515,7 +515,7 @@ testStatistic.pairedTTest(sample1, sample2, .05);
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returned by <code>testStatistic.pairedTTest(sample1, sample2)</code>
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is less than <code>.05</code>
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</dd>
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<dd><strong>Example 2: </strong> unpaired, two-sample t-test using
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<dd><strong>Example 2: </strong> unpaired, two-sided, two-sample t-test using
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<code>StatisticalSummary</code> instances, without assuming that
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subpopulation variances are equal.
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<p>
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@ -543,7 +543,7 @@ testStatistic.t(summary1, summary2);
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</source>
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</p>
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<p>
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To compute the (one-sided) p-value:
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To compute the p-value:
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<source>
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testStatistic.tTest(sample1, sample2);
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</source>
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