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Unconditional Tests (unconditional + test)
Selected AbstractsA Cautionary Note on Exact Unconditional Inference for a Difference between Two Independent Binomial ProportionsBIOMETRICS, Issue 2 2003Devan V. Mehrotra Summary Fisher's exact test for comparing response proportions in a randomized experiment can be overly conservative when the group sizes are small or when the response proportions are close to zero or one. This is primarily because the null distribution of the test statistic becomes too discrete, a partial consequence of the inference being conditional on the total number of responders. Accordingly, exact unconditional procedures have gained in popularity, on the premise that power will increase because the null distribution of the test statistic will presumably be less discrete. However, we caution researchers that a poor choice of test statistic for exact unconditional inference can actually result in a substantially less powerful analysis than Fisher's conditional test. To illustrate, we study a real example and provide exact test size and power results for several competing tests, for both balanced and unbalanced designs. Our results reveal that Fisher's test generally outperforms exact unconditional tests based on using as the test statistic either the observed difference in proportions, or the observed difference divided by its estimated standard error under the alternative hypothesis, the latter for unbalanced designs only. On the other hand, the exact unconditional test based on the observed difference divided by its estimated standard error under the null hypothesis (score statistic) outperforms Fisher's test, and is recommended. Boschloo's test, in which the p-value from Fisher's test is used as the test statistic in an exact unconditional test, is uniformly more powerful than Fisher's test, and is also recommended. [source] Voluntary Disclosure, Earnings Quality, and Cost of CapitalJOURNAL OF ACCOUNTING RESEARCH, Issue 1 2008JENNIFER FRANCIS ABSTRACT We investigate the relations among voluntary disclosure, earnings quality, and cost of capital. We find that firms with good earnings quality have more expansive voluntary disclosures (as proxied by a self-constructed index of coded items found in 677 firms' annual reports and 10-K filings in fiscal 2001) than firms with poor earnings quality. In unconditional tests, we find that more voluntary disclosure is associated with a lower cost of capital. However, consistent with the complementary association between disclosure and earnings quality, we find that the disclosure effect on cost of capital is substantially reduced or disappears completely (depending on the cost of capital proxy) once we condition on earnings quality. Extensions probing alternative proxies show that our findings are robust to measures of earnings quality and cost of capital, but not to other measures of voluntary disclosure. In particular, we find opposite relations for voluntary disclosure measures based on management forecasts and conference calls, and we find no relations for a press release based measure. [source] Asymptotical Tests on the Equivalence, Substantial Difference and Non-inferiority Problems with Two ProportionsBIOMETRICAL JOURNAL, Issue 3 2004A. Martín Andrés Abstract Let d = p2 , p1 be the difference between two binomial proportions obtained from two independent trials. For parameter d, three pairs of hypothesis may be of interest: H1: d , , vs. K1: d > ,; H2: d ,, (,1, ,2) vs. K2: d , (,1, ,2); and H3: d , [,1, ,2] vs. K3: d ,, [,1, ,2], where Hi is the null hypothesis and Ki is the alternative hypothesis. These tests are useful in clinical trials, pharmacological and vaccine studies and in statistics generally. The three problems may be investigated by exact unconditional tests when the sample sizes are moderate. Otherwise, one should use approximate (or asymptotical) tests generally based on a Z -statistics like those suggested in the paper. The article defines a new procedure for testing H2 or H3, demonstrates that this is more powerful than tests based on confidence intervals (the classic TOST , two one sided tests , test), defines two corrections for continuity which reduce the liberality of the three tests, and selects the one that behaves better. The programs for executing the unconditional exact and asymptotic tests described in the paper can be loaded at http://www.ugr.es/~bioest/software.htm. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] A Cautionary Note on Exact Unconditional Inference for a Difference between Two Independent Binomial ProportionsBIOMETRICS, Issue 2 2003Devan V. Mehrotra Summary Fisher's exact test for comparing response proportions in a randomized experiment can be overly conservative when the group sizes are small or when the response proportions are close to zero or one. This is primarily because the null distribution of the test statistic becomes too discrete, a partial consequence of the inference being conditional on the total number of responders. Accordingly, exact unconditional procedures have gained in popularity, on the premise that power will increase because the null distribution of the test statistic will presumably be less discrete. However, we caution researchers that a poor choice of test statistic for exact unconditional inference can actually result in a substantially less powerful analysis than Fisher's conditional test. To illustrate, we study a real example and provide exact test size and power results for several competing tests, for both balanced and unbalanced designs. Our results reveal that Fisher's test generally outperforms exact unconditional tests based on using as the test statistic either the observed difference in proportions, or the observed difference divided by its estimated standard error under the alternative hypothesis, the latter for unbalanced designs only. On the other hand, the exact unconditional test based on the observed difference divided by its estimated standard error under the null hypothesis (score statistic) outperforms Fisher's test, and is recommended. Boschloo's test, in which the p-value from Fisher's test is used as the test statistic in an exact unconditional test, is uniformly more powerful than Fisher's test, and is also recommended. [source] | ||||||||||