The run test for two samples in the presence of uncertainty

Abstract The run test, which examines whether two samples selected from the same population are random, has been employed. However, the current run test for two samples is based on the assumption of certainty, which is not always valid in practical scenarios. This paper aims to introduce a modified...

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Main Author: Muhammad Aslam
Format: Article
Language:English
Published: SpringerOpen 2023-11-01
Series:Journal of Big Data
Subjects:
Online Access:https://doi.org/10.1186/s40537-023-00850-0
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author Muhammad Aslam
author_facet Muhammad Aslam
author_sort Muhammad Aslam
collection DOAJ
description Abstract The run test, which examines whether two samples selected from the same population are random, has been employed. However, the current run test for two samples is based on the assumption of certainty, which is not always valid in practical scenarios. This paper aims to introduce a modified version of the run test for two samples that account for uncertainty. We will develop a statistical approach for the run test that considers uncertain factors such as sample size, level of significance, and observations. To evaluate the effectiveness of the proposed test, we analyze wind power and photovoltaic power data. The analysis of these variables demonstrates that they are randomly selected from the population. The results indicate that the proposed run test is well-suited for addressing uncertainty in renewable energy. By employing this modified test, we can effectively assess the randomness of samples and make reliable conclusions in uncertain conditions.
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spelling doaj.art-95e98754d338419b8be786250ad5ac552023-11-05T12:21:00ZengSpringerOpenJournal of Big Data2196-11152023-11-011011910.1186/s40537-023-00850-0The run test for two samples in the presence of uncertaintyMuhammad Aslam0Department of Statistics, Faculty of Science, King Abdulaziz UniversityAbstract The run test, which examines whether two samples selected from the same population are random, has been employed. However, the current run test for two samples is based on the assumption of certainty, which is not always valid in practical scenarios. This paper aims to introduce a modified version of the run test for two samples that account for uncertainty. We will develop a statistical approach for the run test that considers uncertain factors such as sample size, level of significance, and observations. To evaluate the effectiveness of the proposed test, we analyze wind power and photovoltaic power data. The analysis of these variables demonstrates that they are randomly selected from the population. The results indicate that the proposed run test is well-suited for addressing uncertainty in renewable energy. By employing this modified test, we can effectively assess the randomness of samples and make reliable conclusions in uncertain conditions.https://doi.org/10.1186/s40537-023-00850-0Renewable energyRun testPopulationClassical statisticsNeutrosophy
spellingShingle Muhammad Aslam
The run test for two samples in the presence of uncertainty
Journal of Big Data
Renewable energy
Run test
Population
Classical statistics
Neutrosophy
title The run test for two samples in the presence of uncertainty
title_full The run test for two samples in the presence of uncertainty
title_fullStr The run test for two samples in the presence of uncertainty
title_full_unstemmed The run test for two samples in the presence of uncertainty
title_short The run test for two samples in the presence of uncertainty
title_sort run test for two samples in the presence of uncertainty
topic Renewable energy
Run test
Population
Classical statistics
Neutrosophy
url https://doi.org/10.1186/s40537-023-00850-0
work_keys_str_mv AT muhammadaslam theruntestfortwosamplesinthepresenceofuncertainty
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