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Tests of Hypotheses
Published in Lawrence S. Aft, Fundamentals of Industrial Quality Control, 2018
The nature of the inequality determines whether a test is one-tailed or two-tailed, a fact that is important in later steps of the analysis. When the alternative hypothesis is stated as a directional inequality the procedure is called a one-tailed test of hypothesis.A nondirectional inequality in the alternative hypothesis signifies a two-tailed test of hypothesis.
Guidelines for Statistical Procedure
Published in Sam A. Hout, Manufacturing of Quality Oral Drug Products, 2022
A one- or two-tailed evaluation must be specified to choose the acceptance region. A one-tailed test is used when the average must fall on one side of the mean. If the test results must be either higher or lower than the current process, a one-tailed test is used. The two-tailed test is used if the test mean may fall on either side of the control mean. The number of tails is important when choosing the acceptance region for the test. A two-tail test will require the alpha level to be divided by two to capture equal areas on either side of the mean.
Statistics Basics
Published in Giorgio Luciano, Statistical and Multivariate Analysis in Material Science, 2021
When we need to establish whether one experimental value is significantly greater than another, or the other way around, we recur to a one-tailed test, while when we need to establish whether there is a significant difference between the two values being compared, we make use of the two-tailed test.
Disaster readiness’ influence on the impact of supply chain resilience and robustness on firms’ financial performance: a COVID-19 empirical investigation
Published in International Journal of Production Research, 2023
Table 6 depicts the results of the PLS structural model analysis. We employed the bootstrapping method that stabilises the β coefficient estimates based on the 95% bias corrected confidence interval. We tested the hypotheses using a one-tailed test based on the recommendations of several scholars (Latan et al. 2018; Lopes de Sousa Jabbour et al. 2020b; Wooldridge 2020). Indeed, a one-tailed test is more appropriate when the hypotheses’ directions are clear, thus reducing type II errors (Latan et al. 2018; Wooldridge 2020).
Fostering low-carbon production and logistics systems: framework and empirical evidence
Published in International Journal of Production Research, 2021
Ana Beatriz Lopes de Sousa Jabbour, Charbel Jose Chiappetta Jabbour, Joseph Sarkis, Hengky Latan, David Roubaud, Moacir Godinho Filho, Maciel Queiroz
We tested our hypotheses in view of the coefficient parameter and the significant value generated from the 95% bias-corrected confidence interval (BCa) of each independent variable. We tested the hypotheses using the one-tailed rather than two-tailed test. Testing hypotheses using a one-tailed test is more appropriate when the hypothesised direction is clear, so as to minimise type II errors (Field 2016; Latan et al. 2018; Wooldridge 2020). The results of the testing of hypotheses are presented in Table 8 below.