Search

If decisions of marketing managers are to be influenced by predictions based on theoretical models, it is important that the assumptions in the models be accurate. Marketeers must reformulate concepts in such models to make them empirically meaningful, as the author of this article shows. [ABSTRACT FROM AUTHOR]

It is generally recognized that when deciding on a significance level for a test, its power must also be taken into account. Determination of the optimal significance level, however, is not a straightforward task. It is pointed out here that by use of critical significance level and power we can achieve the usual objective of making a reject-or-accept decision without explicitly determining the optimal significance level. [ABSTRACT FROM AUTHOR]

In hypothesis testing, the level of significance, α, often performs a screening as well as an evaluating function. Results which are not "significant" at some preset value of α are simply not reported. Unfortunately, this value of α is usually arbitrarily set at .05 or .01 and bears no relation to the needs of potential users of the study. This paper suggests an approach to making the selection of α more responsive to these potential applications. [ABSTRACT FROM AUTHOR]

Cronholm and Revusky [4] have proposed a rank test based on independent subexperiments for the comparison of a treatment effect with a control when the treatment is destructive but the control produces at most a transient effect. This article provides a thorough study of its performance under two sampling situations. With the aid of a computer, the exact small sample power of the test is evaluated for some important alternatives. A modification of the test is proposed for the situation where numerical measurements are available rather than merely the ranks within each stage. The modification is found to substantially improve the power. Both tests are shown to be unbiased. Finally, the Pitman asymptotic efficiencies are obtained and comparisons are made with the appropriate Wilcoxon tests. [ABSTRACT FROM AUTHOR]

A double sample test for a hypothesis concerning the mean mu of a normal random variable with unknown variance sigma[sup 2] is presented in this note. This test is an alternative to the usual single sample t test and is made by taking samples at two stages. After the first sample has been observed the hypothesis is rejected, accepted or a second sample is taken. This double sample test is a modification of a double sample test suggested by D. Owen. However, for the test being suggested here if the second sample is taken the information from the first sample is used in making the final decision. A table is included which is a tabulation of the rejection and acceptance points for various sample sizes and alpha = .05 and alpha = .01. Additional tables are included which give some power values and expected sample sizes for the new test and Owen's test. These power values are compared with power values of various single sample t tests. Conclusions about desirable sample sizes are presented for both the new test and Owen's test. [ABSTRACT FROM AUTHOR]

In an earlier paper [5] the authors compared the power of the BLUS test with the probability of a correct decision of the Durbin-Watson bounds test. A method to compute the distribution of the Von Neumann ratio under the null hypothesis and under the alternative hypothesis was given. In the present paper the latter method is used to tabulate the BLUS-test statistic and to compute the exact significance points of the Durbin-Watson test for several examples. Powers of both tests are computed and compared. It appears that, for the cases considered, the power of the exact Durbin-Watson test exceeds that of the BLUS procedure, while the latter is greater than the probability of a correct decision in the Durbin-Watson bounds test. [ABSTRACT FROM AUTHOR]

The problem of iterated tests of hypotheses has been treated from the standpoint of multiple decision theory. Under the assumption that there is a UMP test (not necessarily similar) for each component hypothesis, a procedure has been developed which ensures an upper bound for various probabilities of misclassification and which maximizes the probabilities of correct classification in some class. [ABSTRACT FROM AUTHOR]

The purpose of this paper was to develop a set of factors which would enable one to answer questions regarding specific alpha and beta values, whether they should be large or small errors, and to what kinds of decisions are these errors related. The set of factors found useful in thinking through the difficulties encountered are: 1) number of alternations, 2) planning horizon, 3) past success of decision-maker, and 4) cost-revenue consequences of an action. In classical hypothesis testing there are no rules that allow one to systematically set alpha and beta error levels. Generally, type I error is set at 5 per cent (alpha per cent) and type II error is almost ignored. A number of people in diverse areas of specialization have drawn attention to the unsystematic treatment in setting alpha and beta in classical hypothesis testing. Important too is the confusion between classical statistical inference and Bayesian statistical decision-making. Selecting error levels has been made difficult by a failure to distinguish between decision and inferential problems. The difference between the expected costs of proposed projects and the expected costs associated with reducing error levels is also highlighted since this difference has led to some confusion in establishing error levels. A next step is to test whether this approach materially improves the results of hypothesis testing in organizations. Hopefully, others will be stimulated to pursue a similar line of investigation, or to provide alternative approaches. [ABSTRACT FROM AUTHOR]

The problem of introducing a consumer product into the market is considered as one of "testing" the product in some restricted group of marketing areas and utilizing the information thereby gained in drawing inference to potential new market areas. A decision rule is developed for classifying proposed expansion areas as potentially penetrable or impenetrable for the product using minimization of the expected cost of error as the optimality criterion. A significance test for the classification is considered along with the problems of ranking areas within the two categories, classifying groups of areas, and analyzing the sensitivity of the solution. [ABSTRACT FROM AUTHOR]

The article focuses on a prefactor analysis of a series of possible attitude-clusters or constructs and a suggested framework for linking these constructs may help in predicting foreign-policy decisional behavior and also in finding regularities in past decision-making behavior. Two schools of decision making have been presented. One is the Morgenthau school which is blamed for over-simplification and the other is the Snyder school known for its unmanageable complexities. A generalization about decision-making behavior is required for the sake of replicability and parsimony. Attempts to identify and structure common personality regularities which act towards the foreign policy environment have been made. The term constructs seems best for present purposes as it suggests an abstract structure for constructing environment. Moreover, it emphasizes the hypothetical nature of the attitude-clusters. Actual behavior is the independent variable from which constructs are inferred. The hope for a science of decision-making lies in the fact that humans demonstrate temporal constancy and strive for internal consistency in their reactions to their environment.

Explores the use of statistical tests for decision-making in regional geographical construction in the United States. Chi square; Variance analysis; Relationship between regional constructs and the areal dispersion of selected traits within regional frames.