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To explore the appropriateness of statistical significance testing to measure the practical, managerial significance of outcomes in marketing programmes.

An in‐depth analysis of SST's scientific roots is coupled with delineation of a set of general objectives of marketing‐programme measurement to identify the applicability limits of significance testing.

In particular, it is shown that the relatively well known sample‐size dependence of SST and its somewhat lesser known replicability, representativeness and impact fallacies can severely affect the robustness of significance tests. Statistical significance is not the same concept as practical significance.

Comprehensive discussion of principles and practice leads to a set of prescriptive usage recommendations, directed at the goal of establishing much‐needed applicability rules and limits for the use of significance‐testing methodologies in an applied marketing context.

This robust challenge to the efficacy of significance testing in marketing practice should be of interest to any marketing planner concerned with the collection and use of marketing intelligence.

Organizational decision‐makers require information presented in ways that allow them to make informed decisions on the effectiveness of change interventions. Current statistical methods do not provide enough information about the practical value of organizational interventions to decision‐makers. It is proposed that a strong hypothesis testing strategy provides a partial answer to this problem. The hypothesis testing method presented here uses Bayesian statistics to test null hypotheses other than the traditional Ho = 0. A description of the evaluation of a change project in six manufacturing plants of a large United States corporation is provided. The data from this project is used to show how both statistical and practical significance may be tested using this hypothesis testing method. The applicability of the strong hypothesis testing approach to the assessment of organizational change is then discussed, and recommendations are made for evaluations conducted in field settings.

Describes statistical methods applied to sensory discrimination tests. Illustrates binomial and chi‐square statistical analysis and discusses similarity testing, power and replication in discrimination testing.

The purpose of this paper is to consider the concepts of individual and complete statistical power used for multiple testing and shows their relevance for determining the number of statistical tests to perform when assessing non-response bias.

A statistical power analysis of 55 survey-based research papers published in three prestigious logistics journals (International Journal of Physical Distribution and Logistics Management, Journal of Business Logistics, Transportation Journal) over the last decade was conducted.

Results show that some of the low complete power levels encountered could have been avoided if fewer tests had been used in the assessment of non-response bias.

The research offers important recommendations to scholars engaged in survey research as they assess the effects of non-respondents on research findings. By following the recommended strategies for testing non-response bias, researchers can improve the statistical power of their findings.

The purpose of this paper is threefold. First, the paper demonstrates how inter-ocular testing (looking at the data) of Schwartz values from world values study (WVS) provides a surprisingly different picture to what the authors would expect from traditional mean comparison testing (t-tests, analysis of variance (ANOVA)). Second, the authors suggest that the ReVaMB model can be applied to an East Asian philosophical perspective. Relativity, the authors argue, is a factor when East Asian wisdom, philosophies and ideologies (Confucianism, Taoism, Buddhism and Legalism) “drive” outcomes such as work ethic. Third, the paper serves as an editorial to a special issue in CCSM on East Asian wisdom and its impact on business culture and performance in a cross-cultural context. Common themes are Yin Yang, how different cultures deal with paradox, and Zhong Yong, with accompanying concerns of how to conceptualise and deal with balance of opposites.

The authors adopted ten variables of the Schwartz values scales used in the WVS and subjected them to principle components analysis to reduce the number of variables. The authors found a two-factor solution: one relating to personal material success and adventure and excitement; another relating to success and personal recognition. The authors labelled these factors as Altruism and Hedonism. The analysis is based on an overall sample of 84,692 respondents in 60 countries. In addition to traditional statistical testing, the authors conduct inter-ocular testing. The authors also suggest that the ReVaMB model can be applied to East Asian wisdom.

Three recommendations help to arrive at more accurate conclusions when comparing groups: the authors recommend to aspire to “consistent look and statistic”. If the data distribution does not agree with the statistics, then the researcher should take a closer look. To avoid misinterpreting statistics and other analysis, the authors recommend inter-ocular testing, i.e. eyeballing data in a scientific fashion. The authors provide specific examples how to do that. The authors recommend to test for common-language effect size (CLE), and also recommend a new rule of thumb, i.e. a split of 60/40 as minimum difference to make any generalisation; 70/30 is worth considering. The rule of thumb contributes to better differentiation between real and “not real” differences.

The authors introduce two concepts: the “inter-ocular test”, which simply means to “look at your data”, and the Chinese word, 错觉 (Cuòjué) which roughly translates to “illusion”, “wrong impression”, or “misconception”. This study argues against accepting simplistic averages for data analysis. The authors provide evidence that an inter-ocular test provides a more comprehensive picture of data when comparing groups rather than simply relying on traditional statistical mean comparison testing. The “word of caution” is to avoid premature conclusions on group comparisons with statistical testing alone. The authors also propose an extension of the original ReVaMB model from a confucian orientation to a broad East Asian philosophical perspective. Culture does determine attitudes and behaviour which in turn contribute to the shaping of cultures, depending on situation, context, location and time. The “context” for a situation to occur should be tested as moderators, for example, between East Asian wisdom (Confucianism, Taoism, Buddhism and Legalism) and behavioural or attitudinal dimensions such as work ethic.

The overall goal of science is to build a valid and reliable body of knowledge about the functioning of the world and how applying that knowledge can change it. As personnel and human resources management researchers, we aim to contribute to the respective bodies of knowledge to provide both employers and employees with a workable foundation to help with those problems they are confronted with. However, what research on research has consistently demonstrated is that the scientific endeavor possesses existential issues including a substantial lack of (a) solid theory, (b) replicability, (c) reproducibility, (d) proper and generalizable samples, (e) sufficient quality control (i.e., peer review), (f) robust and trustworthy statistical results, (g) availability of research, and (h) sufficient practical implications. In this chapter, we first sing a song of sorrow regarding the current state of the social sciences in general and personnel and human resources management specifically. Then, we investigate potential grievances that might have led to it (i.e., questionable research practices, misplaced incentives), only to end with a verse of hope by outlining an avenue for betterment (i.e., open science and policy changes at multiple levels).

Discusses alternative strategies which may be employed when differences arise between achieved audit‐sampling results and planned results, which means that risk levels used in ex post decision making may be different from planned levels. Contrasts a conventional strategy — which is to fix the risk of incorrect acceptance at a planned level and to ignore the risk of incorrect rejection or to accept the minimum available level of that risk which is consistent, after the fact, with the planned level of risk of incorrect acceptance — with a theoretically appealing strategy which balances both risk levels in proportion to their perceived disutility. Reports on the results of an experiment involving these two strategies, in which all subjects were auditors with statistical audit experience. Suggests that the most important statistically significant finding is that, in certain circumstances, these auditors are more willing to base audit decisions on statistical evidence after the alternative strategy is explained and available for their use.

This chapter describes the complementary benefits of model-building and data analysis using algorithm and statistical modeling methods in the context of unobtrusive marketing field experiments and in transforming findings into isomorphic-management models. Relevant for marketing performance measurement, case-based configural analysis is a relatively new paradigm in crafting and testing theory. Statistical testing of hypotheses to learn net effects of individual terms in MRA equations is the current dominant logic. Isomorphic modeling might best communicate what executives should decide using the findings from algorithm and statistical models. Data testing these propositions here uses data from an unobtrusive field experiment in a retailing context and includes two levels of expertise, four price points, and presence versus absence of a friend (“pal” condition) during the customer-salesperson interactions (n = 240 store customers). The analyses support the conclusion that all three approaches to modeling provide useful complementary information substantially above the use of one or the other alone and that transforming findings from such models into isomorphic-management models is possible.