BackgroundEvidence for the efficacy of cognitive–behavioural therapy for schizophrenia is promising but evidence for clinical effectiveness is limited.AimsTo test the effectiveness of cognitive–behavioural therapy delivered by clinical nurse specialists in routine practice.MethodOf 274 referrals, 66 were allocated randomly to 9 months of treatment as usual (TAU), cognitive–behavioural therapy plus TAU (CBT) or supportive psychotherapy plus TAU (SPT) and followed up for 3 months.ResultsTreatment effects were modest but the CBT condition gave significantly greater improvement in overall symptom severity than the SPT or TAU conditions combined (F (1,53)=4.14; P=0.05). Both the CBT and SPT conditions combined gave significantly greater improvement in severity of delusions than did the TAU condition (F (1,53)=4.83; P=0.03). Clinically significant improvements were achieved by 7/21 in the CBT condition (33%), 3/19 in the SPT condition (16%) and 2/17 in the TAU condition (12%).ConclusionsCognitive–behavioural therapy delivered by clinical nurse specialists is a helpful adjunct to routine care for some people with chronic psychosis.

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This paper argues that a Fisherian approach to statistical inference, which views statistical testing as determining the chance probability of an event, is coherent, consistent with modern statistical methods and forms a sound theoretical basis for the use of statistical tests in psychology. It is argued that Fisherian statistical tests are concerned with establishing the direction of tested effects, give rise to confidence intervals and are quite consistent with power analyses. Researchers are encouraged to report chance probabilities, and to interpret them according to the prevailing conditions rather than using a fixed decision rule. The contrasting Neyman–Pearson approach, which views statistical testing as a quality control procedure for accepting hypotheses, posits unreasonable research practices which psychologists do not and should not be expected to follow. Neyman–Pearson theory has caused confusion in the psychological literature and criticisms have been levelled at statistical testing in general that ought to have been directed specifically at Neyman–Pearson testing. Statistical inference, of any sort, is held to be insufficient to characterize the process of testing scientific hypotheses. Data should be seen as evidence to be used in psychological arguments and statistical significance is just one measure of its quality. It restrains researchers from making too much of findings which could otherwise be explained by chance.