The cosmic curvature Ω_(K,0),which determines the spatial geometry of the universe,is an important parameter in modern cosmology.Any deviation from Ω_(K,0)=0 would have a profound impact on the primordial inflation ...The cosmic curvature Ω_(K,0),which determines the spatial geometry of the universe,is an important parameter in modern cosmology.Any deviation from Ω_(K,0)=0 would have a profound impact on the primordial inflation paradigm and fundamental physics.In this work,we adopt a cosmological model-independent method to test whether Ω_(K,0) deviates from zero.We use the Gaussian process to reconstruct the reduced Hubble parameter E(z)and the derivative of the distance D'(z)from observational data and then determine Ω_(K,0) with a null test relation.The cosmic chronometer(CC)Hubble data,baryon acoustic oscillation(BAO)Hubble data,and supernovae Pantheon sample are considered.Our result is consistent with a spatially flat universe within the domain of reconstruction 0<z<2.3,at the 1σ confidence level.In the redshift interval 0<z<1,the result favors a flat universe,while at z>1,it tends to favor a closed universe.In this sense,there is still a possibility for a closed universe.We also carry out the null test of the cosmic curvature at 0<z<4.5 using the simulated gravitational wave standard sirens,CC+BAO,and redshift drift Hubble data.The result indicates that in the future,with the synergy of multiple highquality observations,we can tightly constrain the spatial geometry or exclude the flat universe.展开更多
An important problem with null hypothesis significance testing, as it is normally performed, is that it is uninformative to reject a point null hypothesis [1]. A way around this problem is to use range null hypotheses...An important problem with null hypothesis significance testing, as it is normally performed, is that it is uninformative to reject a point null hypothesis [1]. A way around this problem is to use range null hypotheses [2]. But the use of range null hypotheses also is problematic. Aside from the usual issues of whether null hypothesis significance tests can be justified at all, there is an issue that is specific to range null hypotheses. It is not straightforward how to calculate the probability of the data given a range null hypothesis. The traditional way is to use the single point that maximizes the obtained p-value. The Bayesian alternative is to propose a prior probability distribution and integrate across it. Because frequentists and Bayesians disagree about a variety of issues, especially those pertaining to whether it is permissible to assign probabilities to hypotheses, and what gets lost in the shuffle is that the two camps actually come to different answers for the probability of the data given a range null hypothesis. Because the probability of the data given the hypothesis is a precursor for both camps, for drawing conclusions about hypotheses, different values for this probability for the different camps is crucial but seldom acknowledged. The goal of the present article is to bring out the problem in a manner accessible to researchers without strong mathematical or statistical backgrounds.展开更多
P values based on standard hypothesis testing are commonly reported in articles published by the Journal of Forestry Research(JFR).However,effect sizes are barely used and reported,even if they are of direct relevance...P values based on standard hypothesis testing are commonly reported in articles published by the Journal of Forestry Research(JFR).However,effect sizes are barely used and reported,even if they are of direct relevance to the primary questions of many of the published studies.The incorporation of effect sizes in studies published by JFR should be encouraged and promoted.Inclusion of effect sizes as a requirement in the journal guidelines will facilitate a major change in the way data are tested and interpreted,with the ultimate goal to exempt researchers from the custom of drawing conclusions merely based upon a dichotomous statistical result(P value).Such a policy can also lead to more informed decisions of whether identified effects are of practical relevance to the forestry.展开更多
基金Supported by the National SKA Program of China(2022SKA0110200,2022SKA0110203)the National Natural Science Foundation of China(11975072,11835009,11875102)。
文摘The cosmic curvature Ω_(K,0),which determines the spatial geometry of the universe,is an important parameter in modern cosmology.Any deviation from Ω_(K,0)=0 would have a profound impact on the primordial inflation paradigm and fundamental physics.In this work,we adopt a cosmological model-independent method to test whether Ω_(K,0) deviates from zero.We use the Gaussian process to reconstruct the reduced Hubble parameter E(z)and the derivative of the distance D'(z)from observational data and then determine Ω_(K,0) with a null test relation.The cosmic chronometer(CC)Hubble data,baryon acoustic oscillation(BAO)Hubble data,and supernovae Pantheon sample are considered.Our result is consistent with a spatially flat universe within the domain of reconstruction 0<z<2.3,at the 1σ confidence level.In the redshift interval 0<z<1,the result favors a flat universe,while at z>1,it tends to favor a closed universe.In this sense,there is still a possibility for a closed universe.We also carry out the null test of the cosmic curvature at 0<z<4.5 using the simulated gravitational wave standard sirens,CC+BAO,and redshift drift Hubble data.The result indicates that in the future,with the synergy of multiple highquality observations,we can tightly constrain the spatial geometry or exclude the flat universe.
文摘An important problem with null hypothesis significance testing, as it is normally performed, is that it is uninformative to reject a point null hypothesis [1]. A way around this problem is to use range null hypotheses [2]. But the use of range null hypotheses also is problematic. Aside from the usual issues of whether null hypothesis significance tests can be justified at all, there is an issue that is specific to range null hypotheses. It is not straightforward how to calculate the probability of the data given a range null hypothesis. The traditional way is to use the single point that maximizes the obtained p-value. The Bayesian alternative is to propose a prior probability distribution and integrate across it. Because frequentists and Bayesians disagree about a variety of issues, especially those pertaining to whether it is permissible to assign probabilities to hypotheses, and what gets lost in the shuffle is that the two camps actually come to different answers for the probability of the data given a range null hypothesis. Because the probability of the data given the hypothesis is a precursor for both camps, for drawing conclusions about hypotheses, different values for this probability for the different camps is crucial but seldom acknowledged. The goal of the present article is to bring out the problem in a manner accessible to researchers without strong mathematical or statistical backgrounds.
基金co-supported by the Outstanding Action Plan of Chinese Sci-tech Journals(Grant No.OAP–C–077)the Startup Foundation for Introducing Talent of Nanjing University of Information Science&Technology(NUIST),Nanjing,China(Grant No.003080)the Jiangsu Distinguished Professor Program of the People’s Government of Jiangsu Province。
文摘P values based on standard hypothesis testing are commonly reported in articles published by the Journal of Forestry Research(JFR).However,effect sizes are barely used and reported,even if they are of direct relevance to the primary questions of many of the published studies.The incorporation of effect sizes in studies published by JFR should be encouraged and promoted.Inclusion of effect sizes as a requirement in the journal guidelines will facilitate a major change in the way data are tested and interpreted,with the ultimate goal to exempt researchers from the custom of drawing conclusions merely based upon a dichotomous statistical result(P value).Such a policy can also lead to more informed decisions of whether identified effects are of practical relevance to the forestry.
