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- W4386095650 abstract "Abstract In this study, we investigate sample dependence in statistical hypothesis testing, where samples are assumed and required to be independent. We show that dependent samples produce lower $p$ values than independent samples; thus, they are more likely to produce a false positive in the test than independent samples. To account for sample dependence in hypothesis testing, we use graph theory to quantify the degree of dependence in terms of graph density and model the variance of graph density as a function of sample size. Using the model, we propose a computational method for estimating the adjusted sample size, which can be used as additional information for the dependent samples in hypothesis testing. To support the validity of the proposed method, we simulate model-free and model-based data sets to demonstrate the applicability of the proposed method. We then apply the method to rice genomic data sets of genetic variants across diverse accessions and estimate the adjusted sample size as a guide to account for sample dependence in hypothesis testing." @default.
- W4386095650 created "2023-08-24" @default.
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- W4386095650 date "2023-08-23" @default.
- W4386095650 modified "2023-09-26" @default.
- W4386095650 title "A computational model for sample dependence in hypothesis testing of genomic data" @default.
- W4386095650 doi "https://doi.org/10.21203/rs.3.rs-3274951/v1" @default.
- W4386095650 hasPublicationYear "2023" @default.
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