China
Africa
Explore chapters and articles related to this topic
Common Statistical Approach
Published in Atsushi Kawaguchi, Multivariate Analysis for Neuroimaging Data, 2021
There is a major premise that errors occur in the test. When performing the test, the null hypothesis and the alternative hypothesis are set; the probability value (p-value) is calculated from the data and compared with a preset significance level (normally 5%, so the following is also assumed to be 5%). If the p-value is lower than the significance level, the null hypothesis is rejected (the alternative hypothesis is adopted) and the conclusion that the result is significant (such as a significant difference) is reached. If the p-value exceeds the significance level, the null hypothesis is not rejected. The test performed in this procedure is called a 5% significance level test. As a statistical precept, the p-value calculated from the data at hand is considered to have variations since the data to be analyzed is a finite sample (data of n people) obtained by random sampling. That is, if another sample were taken (usually one sample, which might be hard to imagine), the p-value from that sample may be different from that of the original sample. Assuming a situation where the p-value can take several values depending on the sample, not all will lead to a correct conclusion and a mistake may occur. The test controls the error so that it is small (it cannot be zero) before drawing conclusions.
Introduction to Research
Published in Vinayak Bairagi, Mousami V. Munot, Research Methodology, 2019
Geetanjali V. Kale, J. Jayanth
If the outcome does not support the null hypothesis, we conclude with an alternate hypothesis. One can define a problem using null hypothesis as “There is no relation between the illness of children and change in season.” If the result rejects the hypothesis, an alternate hypothesis is “Illness of children occurs mainly due to change in season.” The null hypothesis is the precise statement about the parameters. Researchers either approve the hypothesis or disapprove the hypothesis. If researcher disproves the null hypothesis, all other possibilities are represented by alternative hypothesis. Null hypothesis does not provide a statistically significant relationship between variable, whereas, alternate hypothesis provides a statistically significant relationship between them.
Statistics
Published in Alexander D. Poularikas, Handbook of Formulas and Tables for Signal Processing, 2018
32.2.1.1 Statistical Hypothesis is a conjecture that a parameter, e.g., = mean of a Gaussian process, is larger than a specific value ( > 75). 32.2.1.2 Alternative Hypothesis is the value of the parameter in 32.2.1.1 is set less than the specific value of 32.2.1.1 ( < 75). 32.2.1.3 Test is a rule we devise that will tell us what decision to make once the experimental values have been determined. Such a rule is called a test of the statistical hypothesis H0: < 75 against the alternative hypothesis H1: > 75. A test leads to a decision to accept or reject the hypothesis under consideration. Critical Region Let C be that subset of the sample space which, in accordance with a prescribed test, leads to the rejection of the hypothesis under consideration. Then C is called the critical region. Power Function The power function of a test that yields the probability that the sample point falls in the critical region C of the test; a function that yields the probability of rejecting the hypothesis under consideration. Power The value of the power function at a parameter point is called the power of the test at that point. Significance Level The significance level of the test (or the size of the critical region C) is the maximum value (supremum) of the power function of the test when H0 is true (H0 is a hypothesis to be tested against an alternative hypothesis H1 in accordance with a prescribed test).
Resilient and sustainable supplier selection: an integration of SCOR 4.0 and machine learning approach
Published in Sustainable and Resilient Infrastructure, 2023
Md Muzahid Khan, Imranul Bashar, Golam Morshed Minhaj, Absar Ishraq Wasi, Niamat Ullah Ibne Hossain
After data collection, we validated the data using the normal distribution. In this phase, we evaluated the eligibility of the gathered data using MINITAB statistical software and fit in the normal distribution. Here the null hypothesis represents that data is normally distributed and the alternative hypothesis represents that data is not normally distributed. We used the p value from the normal distribution of every distinct criterion. A statistically significant result is one with a p-value greater than 0.05, in that case, the null hypothesis is accepted which means the alternative hypothesis is rejected. As we have a total of 41 criteria, all p-values were determined individually. We have found all our p-value to be greater than 0.05, which signifies that the given data is normally distributed.
The retention of information in virtual reality based engineering simulations
Published in European Journal of Engineering Education, 2022
To answer the above research questions a non-inferiority test was used. Ordinarily, the null hypothesis sets out that the measured and expected data sets are not different from each other, whereas the alternative hypothesis is accepted in the case where the difference between the two datasets is statistically significant. In the case of non-inferiority trials, the null hypothesis sets out that the datasets are different (e.g. that the measured is inferior to the expected) and if the difference is not statistically significant the alternative hypothesis is accepted (Walker and Nowacki 2010). For this reason, the non-inferiority test was chosen because the goal of this study is to prove that the new educational approach using VR is no less effective than the equivalent approach currently being employed in the real world, whilst of course still offering the advantages of educational logistics, engagement and learning experiences that may be impossible in a real industrial environment. Non-inferiority tests use an equivalence margin (see section Quiz) to establish if the two datasets are not inferior to each other. Such a test is commonly used in medical sciences but are increasingly being employed in psychology and education research contexts (Lakens, Scheel, and Isager 2018).
Simulated-annealing-based hyper-heuristic for flexible job-shop scheduling
Published in Engineering Optimization, 2022
Kelvin Ching Wei Lim, Li-Pei Wong, Jeng Feng Chin
A null hypothesis is defined such that there is no difference between and , whereas the alternative hypothesis is that a difference exists between and . Based on the results in Table 6, there are 13 positive signs, two negative signs and a tied match. Hence, the p-value is 0.00451. Given that the p-value <0.05, the null hypothesis is rejected. The conclusion is drawn that a difference exists between the medians of the signed differences. This indicates that SA-HH significantly outperforms the SA-HH.