PubMed · 8646646
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M W Enkin. 1996-06-01. Statistical significance.. https://pubmed.ncbi.nlm.nih.gov/8646646/
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In a statistical significance test a scientific problem is expressed by formulating a null hypothesis and an opposite alternative. Construction of an empirical decision rule usually focuses on control of the alpha-error, i.e. the probability of erroneously refusing the null hypothesis. Contrary to the alpha-error, the beta-error is not controlled and in general is of unknown size. Thus in case of a non-significant result the validity of the null hypothesis still may be highly questionable. Such an unwanted outcome of an applied test the researcher should try to avoid by choosing an appropriate study design. In case it occurs nevertheless, it is advised to further evaluate the (non-significant) result. This can be done by calculating confidence intervals of the tested effects. Furthermore the p-value can be interpreted as a metric measure of evidence against the null hypothesis. By means of a posterior power analysis the probability of a significant test result is estimated under the given circumstances. Thus possibly the applied test--under the assumption of actual validity of the alternative--turns out to have had hardly a chance of rejecting the null hypothesis. In this case the non-significant result (pointing towards the null hypothesis) is relativized substantially. On the other hand a large power points to a small probability of a beta-error.
The load of total suspended solids (TSS) is one of the most important parameters for evaluating wet-weather pollution in urban sanitation systems. In fact, pollutants such as heavy metals, polycyclic aromatic hydrocarbons (PAHs), phosphorous and organic compounds are adsorbed onto these particles so that a high TSS load indicates the potential impact on the receiving waters. In this paper, a stochastic model is proposed to estimate the TSS load and its dynamics during rain events. Information on the various simulated processes was extracted from different studies of TSS in urban areas. The model thus predicts the probability of TSS loads arising from combined sewer overflows (CSOs) in combined sewer systems as well as from stormwater in separate sewer systems in addition to the amount of TSS retained in treatment devices in both sewer systems. The results of this TSS model illustrate the potential of the stochastic modeling approach for assessing environmental problems.
Residue-residue contacts are very important in forming protein structure. In this work, we calculated the average probability of residue-residue contacts in 470 globular proteins and analyzed the distribution of contacts in the different interval of residues using Contacts of Structural Units (CSU) and Structural Classification (SCOP) software. It was found that the relationship between the average probability PL and the residue distance L for four structural classes of proteins could be expressed as lgPL=a+b x L, where a and b are coefficients. We also discussed the connection between two aspects of proteins which have equal array residue number and found that the distribution probability was stable (or un-stable) if the proteins had the same (or different) compact (for example synthase) in the same structural class.