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• Background Clinicians need easy methods of screening for cognitive impairment in patients with heart failure. If correlates of cognitive impairment could be identified, more patients with early cognitive impairment could be treated before the problem interfered with adherence to treatment. • Objectives To describe cognitive impairment in patients with heart failure, to explore the usefulness of 4 measures of cognitive impairment, and to assess correlates of cognitive impairment. • Methods A descriptive, correlational design was used. Four screening measures of cognition were assessed in 42 patients with heart failure: Commands subtest and Complex Ideational Material subtest of the Boston Diagnostic Aphasia Examination, Mini-Mental State Examination, and Draw-a-Clock Test. Cognitive impairment was defined as performance less than the standardized (T-score) cutoff point on at least 1 of the 4 measures. Possible correlates of cognitive impairment included age, education, hypotension, fluid overload (serum osmolality • Results Cognitive impairment was detected in 12 (28.6%) of 42 participants. The 4 screening tests varied in effectiveness, but the Draw-a-Clock Test indicated impairment in 50% of the 12 impaired patients. A summed standardized score for the 4 measures was not significantly associated with age, education, hypotension, fluid overload, or dehydration in this sample. • Conclusions Cognitive impairment is relatively common in patients with heart failure. The Draw-a-Clock Test was most useful in detecting cognitive impairment, although it cannot be used to detect problems with verbal learning or delayed recall and should not be used as the sole screening method for patients with heart failure. Correlates of cognitive impairment require further study.

The current debate about the merits of null hypothesis significance testing, even though provocative, is not particularly novel. The significance testing approach has had defenders and opponents for decades, especially within the social sciences, where reliance on the use of significance testing has historically been heavy. The primary concerns have been (1) the misuse of significance testing, (2) the misinterpretation of P values, and (3) the lack of accompanying statistics, such as effect sizes and confidence intervals, that would provide a broader picture into the researcher's data analysis and interpretation. This article presents the current thinking, both in favor and against, on significance testing, the virtually unanimous support for reporting effect sizes alongside P values, and the overall implications for practice and application.