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This paper aims to study the effects of two different types of state skepticism prompts, as well as the effect of the trait of professional skepticism on auditor cognitive performance in a hypothesis-testing task. It examines the effect of a professional skepticism prompt, based on the presumptive doubt view of professional skepticism, as well as the effect of a cheater-detection prompt, based on social contracts theory.

Seventy-eight audit students and 85 practising auditors examine an audit case and determine the evidence needed to test the validity of a management's assertion in a Wason selection task. The experiment manipulates the presence of a professional skepticism prompt and the presence of a cheater-detection prompt. The personality trait of professional skepticism is measured with Hurtt's scale.

The presence of a professional skepticism prompt improves cognitive performance in the sample of students, but not in the sample of auditors. The presence of a cheater-detection prompt has no significant effect on performance in the student or auditor sample. The personality trait of professional skepticism is a significant predictor of cognitive performance in the sample of students but not in the sample of auditors.

Results suggest that increasing the states of skepticism or suspicion toward the client firm's management may have no incremental effect on the normative hypothesis testing performance of experienced auditors. However, actively encouraging skeptical mindsets in novice auditors is likely to improve their cognitive performance in hypothesis testing tasks.

The study is the first to examine the joint effects of two specific types of state skepticism prompts, a professional skepticism prompt and a cheater-detection prompt, as well as the effect of the personality trait of professional skepticism, on auditor cognitive performance in a hypothesis-testing task. The study contributes to the literature by bringing together the psychology theory of social contracts and auditing research on professional skepticism, to examine auditors' reasoning performance in a hypothesis-testing task.

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.

Extant literature on entrepreneurial cognition declares that entrepreneurs who are confident in their ability to design a new business perform better than entrepreneurs who lack such a self-perception of efficacy. This is swagger. A different set of literature, including Discovery-Driven Planning, Design Thinking, and Lean Startup Method, recommends that entrepreneurs create, confirm, or reject hypotheses to design and refine the specific elements of their business model. This is the scientific method.

This article used survey data from 353 participants in an international business pitch competition to connect these two literatures. We found that the number of hypotheses that the entrepreneur elucidated and confirmed were linked to business model performance. Counter-intuitively, the number of hypotheses rejected by the entrepreneur showed the strongest relationship to success. We found no significant relationship between the number of interviews that an entrepreneur conducted and the business model’s performance: more effort was not always helpful.

Although we found no direct connection between an entrepreneur’s self-efficacy in searching for a new idea and the business model’s eventual success, entrepreneurs with high levels of this narrow form of self-­confidence were more likely to perform the constructive actions of elucidating, confirming, and rejecting hypotheses. In summary, swagger leads to science, and science leads to success.

The purpose of this paper is to show that necessary condition hypotheses are important in operations management (OM), and to present a consistent methodology for building and testing them. Necessary condition hypotheses (“X is necessary for Y”) express conditions that must be present in order to have a desired outcome (e.g. “success”), and to prevent guaranteed failure. These hypotheses differ fundamentally from the common co‐variational hypotheses (“more X results in more Y”) and require another methodology for building and testing them.

The paper reviews OM literature for versions of necessary condition hypotheses and combines previous theoretical and methodological work into a comprehensive and consistent methodology for building and testing such hypotheses.

Necessary condition statements are common in OM, but current formulations are not precise, and methods used for building and testing them are not always adequate. The paper outlines the methodology of necessary condition analysis consisting of two stepwise methodological approaches, one for building and one for testing necessary conditions.

Because necessary condition statements are common in OM, using methodologies that can build and test such hypotheses contributes to the advancement of OM research and theory.

Testing of hypothesis, also known as sample-testing, is a common feature with almost every social and management research. We draw conclusion on population (characteristics) based on available sample information, following certain statistical principles. This paper will introduce the fundamental concepts with suitable examples, mostly in Indian context. This section is expected to help scholar readers, to learn, how hypothesis tests for differences means (or proportions) take different forms, depending on whether the samples are large or small; and also to appreciate hypothesis-testing techniques, on how it could be used in similar decision-making situations, elsewhere.

The issue of model selection in applied research is of vital importance. Since the true model in such research is not known, which model should be used from among various potential ones is an empirical question. There might exist several competitive models. A typical approach to dealing with this is classic hypothesis testing using an arbitrarily chosen significance level based on the underlying assumption that a true null hypothesis exists. In this paper, the authors investigate how successful the traditional hypothesis testing approach is in determining the correct model for different data generating processes using time series data. An alternative approach based on more formal model selection techniques using an information criterion or cross-validation is also investigated.

Monte Carlo simulation experiments on various generating processes are used to look at the response surfaces resulting from hypothesis testing and response surfaces resulting from model selection based on minimizing an information criterion or the leave-one-out cross-validation prediction error.

The authors find that the minimization of an information criterion can work well for model selection in a time series environment, often performing better than hypothesis-testing strategies. In such an environment, the use of an information criterion can help reduce the number of models for consideration, but the authors recommend the use of other methods also, including hypothesis testing, to determine the appropriateness of a model.

This paper provides an alternative approach for selecting the best potential model among many for time series data. It demonstrates how minimizing an information criterion can be useful for model selection in a time-series environment in comparison to some standard hypothesis testing strategies.

This paper deals with concept of total productive maintenance (TPM) and its implementation approach. It also presents the identification of critical factors for effective implementation of TPM. The reliability analysis identified potential areas where more concentration is required. The application of hypothesis testing in productivity maintenance should be promoted by parametric test and significantly instrumental in explanation of phenomena. It is also indispensable to better understand quality data and provide guidance to production control.

The various critical success factors of TPM implementation has organised into set of eight performance measure and thirty three sub-factors for getting the in-depth details of each indicator. The paper identifies the reliability of these factors and understands the problem with greater clarity and its ramification. Researcher collected responses from forty one manufacturing organisations through structured designed questionnaire. The reliability analysis was carriedout by calculating the value of Cronbach's alpha method. To draw the meaningful conclusions supported by relevant empirical data, provisional formulation is required, and it was carried by hypothesis testing. In this test, samples are taken from a population with known distribution (normal distribution), and a test of population parameters is executed. It determines the relevancy of facts directs the researcher's efforts into productive channels. The statements were hypothetically tested by calculating the arithmetic value of Chi-Square (χ²) and MINITAB-19 software was used for identification of p-value.

This study identified that main factors and sub-factors of TPM which are critical for implementation of TPM. The study also avoids the complexities involved in implementing TPM by reliability analysis. It is found that all identified CSFs are reliable as Cronbach's alpha is above 0.6. The hypothesis testing shows that all alternative hypothesis statements are acceptable as Chi-Square (χ²) value has satisfied the conditions and null hypothesis are true as calculated p-value is less than the 0.05 for eight identified TPM critical factor.

In this paper researcher provides a comprehensive typology of TPM-CSFs, and its ranking and importance in manufacturing sector. The preparedness of such study related to TPM implementation is becoming a major sourcing base for the world and there is a paucity of such studies. Such studies are equally important in a global context.

The purpose of this paper is to propose and illustrate blind qualitative hypothesis testing, which is a qualitative research technique that further generalizes the well‐known notion of “blindness” in research to include a qualitative researcher. The technique introduces a method to test a priori hypotheses using qualitative, emergent observation and analysis without the biasing influence of prior knowledge of the hypotheses being tested.

In essence, the proposed technique is as follows. After forming a set of a priori predictive hypotheses, a theoretical researcher (who may or may not be a qualitative researcher) engages the cooperation of a qualitative researcher to perform an empirical study. The qualitative empirical researcher is given adequate guidance to perform a study but is kept blind to the hypotheses. After the qualitative empirical researcher makes observations and forms his or her conclusions, the qualitative empirical researcher and the theoretical researcher jointly determine the extent to which the conclusions support or disconfirm the hypotheses. The qualitative empirical researcher then identifies emergent themes and inductive conclusions that contribute beyond the a priori hypotheses. A study testing consumer response to advertising is described as an illustration of the proposed technique.

The proposed technique diminishes the influence of the ontological assumptions of researchers on hypothesis tests. By reducing a priori expectations, the proposed technique frees practical and academic market researchers to more fully immerse in the context of interest and better recognize subtle phenomena and imbricated, complex intrapersonal and/or social interactions. Furthermore, the proposed technique provides a new way for qualitative methods to go beyond the “supportive” and “exploratory” role to which they have often been limited.

An ability to test hypotheses gives qualitative researchers another way to contribute to the literatures currently dominated by constricted and pallid questionnaire‐based methods within the positivist tradition. Such literatures will benefit from the methodological pluralism encouraged by the technique introduced here because some benefits of qualitative research (including an ability to identify unanticipated, emergent findings) offer much needed compensation for inherent flaws in questionnaire‐based methods.

This survey focuses on two families of nonlinear vector time series models, the family of vector threshold regression (VTR) models and that of vector smooth transition regression (VSTR) models. These two model classes contain incomplete models in the sense that strongly exogeneous variables are allowed in the equations. The emphasis is on stationary models, but the considerations also include nonstationary VTR and VSTR models with cointegrated variables. Model specification, estimation and evaluation is considered, and the use of the models illustrated by macroeconomic examples from the literature.

Turvey (2007, Physica A) introduced a scaled variance ratio procedure for testing the random walk hypothesis (RWH) for financial time series by estimating Hurst coefficients for a fractional Brownian motion model of asset prices. The purpose of this paper is to extend his work by making the estimation procedure robust to heteroskedasticity and by addressing the multiple hypothesis testing problem.

Unbiased, heteroskedasticity consistent, variance ratio estimates are calculated for end of day price data for eight time lags over 12 agricultural commodity futures (front month) and 40 US equities from 2000-2014. A bootstrapped stepdown procedure is used to obtain appropriate statistical confidence for the multiplicity of hypothesis tests. The variance ratio approach is compared against regression-based testing for fractionality.

Failing to account for bias, heteroskedasticity, and multiplicity of testing can lead to large numbers of erroneous rejections of the null hypothesis of efficient markets following an independent random walk. Even with these adjustments, a few futures contracts significantly violate independence for short lags at the 99 percent level, and a number of equities/lags violate independence at the 95 percent level. When testing at the asset level, futures prices are found not to contain fractional properties, while some equities do.

Only a subsample of futures and equities, and only a limited number of lags, are evaluated. It is possible that multiplicity adjustments for larger numbers of tests would result in fewer rejections of independence.

This paper provides empirical evidence that violations of the RWH for financial time series are likely to exist, but are perhaps less common than previously thought.