Test Of Null Hypothesis Research Articles

EFFECTS OF COMPETITIVE AND JIGSAW COLLABORATION LEARNING STRATEGIES ON STUDENTS’ INTEREST IN GEOMETRY AMONG SENIOR SECONDARY SCHOOL STUDENTS IN FCT, ABUJA, NIGERIA

This study investigated the Effects of Competitive and Jigsaw collaborative Learning Strategies on Students’ Interest in Geometry among Senior Secondary School students in Abuja. The study employed a quasi – experimental design using pre-test posttest and non-equivalent control group. A sample of 245 SSII students, comprising of One Hundred and thirty -one (131) males and One Hundred fourteen (114) females were used for the study. A multi stage sampling techniques were used for the study. Instrument for data collection was Students’ Geometry Interest Inventory (SGII) for competitive and jigsaw collaborative learning strategies. The instruments were validated by five experts in mathematics education, three Senior Lectures from Department of Science Education Federal University of Technology, Minna, two Principal Lectures from FCT – COE, Zuba as well as two teachers of mathematics from FCT-SEB. The reliability coefficient of the instruments were obtained using Cronbach Alpha was 0.87 and 0.79 for SGII for competitive and jigsaw collaborative learning strategies. Three research questions were raised and three null hypotheses that were tested at 0.05 level of significant. Ranks Mean were used to answered research questions and Mann Whitney U-test was statistical tool used for testing null hypotheses. The results revealed that the use of competitive learning strategy enhance students’ achievement in geometry. Among other recommendations is that, mathematics teachers should be encouraged to adopt these strategies in the classroom.

Underpowered studies in muscle metabolism research: Determinants and considerations

Biomedical research frequently employs null hypothesis testing to determine whether an observed difference in a sample is likely to exist in the broader population. Null hypothesis testing generally assumes that differences between groups or interventions are non-existent, unless proven otherwise. Because biomedical studies with human subjects are often limited by financial and logistical resources, they tend to have low statistical power, i.e. a low probability of statistically confirming a true difference. As a result, small but potentially clinically important differences may be overseen or ignored simply due to the absence of a statistically significant difference. This absence is often misinterpreted as ‘equivalence’ of treatments. In this educational paper, we will use practical examples related to the effects of exercise and nutrition on muscle protein metabolism to illustrate the most important determinants of statistical power, as well as their implications for both investigators and readers of scientific articles.Changes in muscle mass occur at a relatively slow rate, making it practically challenging to detect differences between treatment groups in a long-term setting. One way to make it ‘easier’ to differentiate between groups and hence increase statistical power is to have a sufficiently long study duration to allow treatment effects to become apparent. This is especially relevant when comparing treatments with relatively small expected differences such as the effect of modest changes in daily protein intake. Secondly, one could try to minimize the variance and response heterogeneity within groups, for example by using strict inclusion criteria and standardization protocols (e.g., meal provision), by using cross-over designs, or even within-subject designs where two interventions are compared simultaneously (e.g., studying an exercised limb vs a contralateral control limb) although this might limit the generalizability of the findings (e.g. such single-limb exercise training is not common in practice). In terms of data interpretation, investigators should obviously refrain from drawing strong conclusions from underpowered studies. Yet, such studies still provide valuable data for meta-analyses. Finally, because muscle protein synthesis rates are highly responsive to anabolic stimuli, acute metabolic studies are more sensitive to detect potentially clinically relevant differences in the anabolic response between treatments. Apart from further elaborating on these topics, this educational article encourages readers to more critically question null findings and scientists to more clearly discuss limitations that may have compromised statistical power.

Multimedia: Multimodal Mediation Analysis of Microbiome Data.

Mediation analysis has emerged as a versatile tool for answering mechanistic questions in microbiome research because it provides a statistical framework for attributing treatment effects to alternative causal pathways. Using a series of linked regressions, this analysis quantifies how complementary data relate to one another and respond to treatments. Despite these advances, existing software's rigid assumptions often result in users viewing mediation analysis as a black box. We designed the multimedia R package to make advanced mediation analysis techniques accessible, ensuring that statistical components are interpretable and adaptable. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis, enabling experimentation with various mediator and outcome models while maintaining a simple overall workflow. The software includes modules for regularized linear, compositional, random forest, hierarchical, and hurdle modeling, making it well-suited to microbiome data. We illustrate the package through two case studies. The first re-analyzes a study of the microbiome and metabolome of Inflammatory Bowel Disease patients, uncovering potential mechanistic interactions between the microbiome and disease-associated metabolites, not found in the original study. The second analyzes new data about the influence of mindfulness practice on the microbiome. The mediation analysis highlights shifts in taxa previously associated with depression that cannot be explained indirectly by diet or sleep behaviors alone. A gallery of examples and further documentation can be found at https://go.wisc.edu/830110.

Exploitation of long-range acoustic dependence for early change detection in dynamic machine running status

This paper suggests that the long-range dependence (LRD) in acoustic signal is an important indicator of dynamic behavior in data. Considering this information, a novel framework based on LRD in acoustic is proposed for detecting change-points in machine running status. Initially, the LRD phenomenon in acoustic is demonstrated. Subsequently, a change-point detection framework is developed, leveraging LRD to monitor machine running states. The framework includes two phases: (1) characteristic data prediction: the LRD value is predicted by the fractional autoregressive integrated moving average (FARIMA) model, which is applied to reflect changes in machine operation states. (2) date change detection: based on residual analysis, a null hypothesis testing based automatic analysis method for acoustic signals during machine successive operations is used to make decision. Experiments in real-world applications demonstrated the good potential of the proposed framework in practical engineering scenarios.

Detecting Exoplanet Transits with the Next Generation of X-Ray Telescopes

Detecting exoplanet transits at X-ray wavelengths would provide a window into the effects of high-energy irradiation on the upper atmospheres of planets. However, stars are relatively dim in the X-ray, making exoplanet transit detections difficult with current X-ray telescopes. To date, only one exoplanet (HD 189733 b) has an X-ray transit detection. In this study, we investigate the capability of future X-ray observatories to detect more exoplanet transits, focusing on both the NewAthena Wide Field Imager instrument and the proposed Advanced X-ray Imaging Satellite (AXIS), which provide more light-collecting power than current instruments. We examined all the transiting exoplanet systems in the NASA Exoplanet Archive and gathered X-ray flux measurements or estimates for each host star. We then predicted the stellar count rates for both AXIS and NewAthena and simulated light curves, using null-hypothesis testing to identify the top 15 transiting planets ranked by potential detection significance. We also evaluate transit detection probabilities when the apparent X-ray radius is enlarged due to atmospheric escape, finding that ≥five of these planetary systems may be detectable on the >4σ level in this scenario. Finally, we note that the assumed host star coronal temperature, which affects the shape of an X-ray transit, can also significantly affect our ability to detect the planet.

P > 0.05 is Good: The NORD-h Protocol for Several Hypothesis Analysis Based on Known Risks, Costs, and Benefits.

Statistical testing in medicine is a controversial and commonly misunderstood topic. Despite decades of efforts by renowned associations and international experts, fallacies such as nullism, the magnitude fallacy, and dichotomania are still widespread within clinical and epidemiological research. This can lead to serious health errors (e.g., misidentification of adverse reactions). In this regard, our work sheds light on another common interpretive and cognitive error: the fallacy of high significance, understood as the mistaken tendency to prioritize findings that lead to low p-values. Indeed, there are target hypotheses (e.g., a hazard ratio of 0.10) for which a high p-value is an optimal and desirable outcome. Accordingly, we propose a novel method that goes beyond mere null hypothesis testing by assessing the statistical surprise of the experimental result compared to the prediction of several target assumptions. Additionally, we formalize the concept of interval hypotheses based on prior information about costs, risks, and benefits for the stakeholders (NORD-h protocol). The incompatibility graph (or surprisal graph) is adopted in this context. Finally, we discuss the epistemic necessity for a descriptive, (quasi) unconditional approach in statistics, which is essential to draw valid conclusions about the consistency of data with all relevant possibilities, including study limitations. Given these considerations, this new protocol has the potential to significantly impact the production of reliable evidence in public health.

Model evaluation: The misuse of statistical techniques when evaluating observations versus predictions

Mathematical modellers, decision support developers, statisticians, and students evaluate the differences between observed and model predicted values. When evaluating models, it is far too easy to conduct model evaluation by fitting a linear regression to the data. In this paper, steps are presented on ‘how to’ evaluate a model using deviance metrics rather than reporting r2 from fitting a linear regression. The paper aims to provide sound reasoning, with data, against using r2. The paper addresses five arguments, previously put forward, for not fitting a linear regression when conducting model evaluation: i) Misapplication of regression; ii) Ambiguity of null hypothesis tests; iii) Lack of sensitivity; iv) Fitted line is irrelevant to validation; and v) Violation of regression assumptions. Statistical, deviance, and quality control metrics are outlined. Three models using the BeefSpecs drafting tool are reported in this paper. Each model (n = 80) had an r2 of 0.43. A mean bias of 0.06, -2.90, and -0.11 mm, and a root mean square error of prediction (RMSEP) of 1.72, 3.37, and 3.70 mm for models 1, 2, and 3, respectively. A modelling efficiency (MEF) of 0.39, -1.34, and -1.83, and 91, 51, and 56% of predictions within upper and lower quality control limits for models 1, 2, and 3, respectively. These metrics highlight the pitfall of reporting r2 from using regression. Minimum recommended steps of ‘how to’ conduct model evaluation are: a plot of the residuals with quality control limits and a table of metrics including mean observed, predicted and bias, RMSEP, and MEF.

Loophole-Free Test of Local Realism via Hardy's Violation.

Bell's theorem states that the quantum mechanical description of physical quantities cannot be fully explained by local realistic theories, laying a solid basis for various quantum information applications. Hardy's paradox is celebrated as the simplest form of Bell's theorem concerning its "All versus Nothing" approach to test local realism. However, due to experimental imperfections, existing tests of Hardy's paradox require additional assumptions of the experimental systems, and these assumptions constitute potential loopholes for faithfully testing local realistic theories. Here, we experimentally demonstrate Hardy's nonlocality through a photonic entanglement source. By achieving a detection efficiency of 82.2%, a quantum state fidelity of 99.10%, and applying high-speed quantum random number generators for the measurement setting switching, the experiment is implemented in a loophole-free manner. During 6h of running, a strong violation of P_{Hardy}=4.646×10^{-4} up to 5 standard deviations is observed with 4.32×10^{9} trials. A null hypothesis test shows that the results can be explained by local realistic theories with an upper bound probability of 10^{-16348}. These testing results provide affirmative evidence against local realism, and establish an advancing benchmark for quantum information applications based on Hardy's paradox.

The US Endangered Species Act and acceptable risk

The fundamental criterion for listing or delisting species under the United States Endangered Species Act (ESA) is acceptable extinction risk. But what is the meaning of “acceptable risk”? This concept has received little attention in conservation biology, and hence, the purpose of this paper is to explain acceptable risk within the context of the ESA. All complex environmental policy decisions involve trade-offs, and hence, choosing one policy from several policy options entails a comparison of the benefits, costs, and risks of all options. Acceptable-risk problems are decision problems requiring a choice among different policy options, and an acceptable risk is defined as the risk associated with the chosen policy. In general, people will accept the risk imposed by a particular policy when that policy yields sufficient net benefits. The ESA, its legislative history, and implementing regulations clearly state that species listing determinations must be based solely on science and “without reference to possible economic or other impacts” caused by the listing of that species. If economic considerations are prohibited from influencing listing decisions, then risk reduction is not constrained by cost, and therefore, the only rational choice for acceptable extinction risk is zero risk. This outcome, known as a “stopping-point problem”, is absurd, and consequently, federal agencies responsible for listing determinations are forced to issue opaque decisions that obscure their true rationale, which in turn, lead to inconsistency and improper political influence. Illustrations of acceptable risk in null hypothesis tests, the United States Clean Air Act, and minimum viable population size are also presented.

Impact of Tile Size and Tile Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification

Popular geo-computer vision works make use of aerial imagery, with sizes ranging from 64 × 64 to 1024 × 1024 pixels without any overlap, although the learning process of deep learning models can be affected by the reduced semantic context or the lack of information near the image boundaries. In this work, the impact of three tile sizes (256 × 256, 512 × 512, and 1024 × 1024 pixels) and two overlap levels (no overlap and 12.5% overlap) on the performance of road classification models was statistically evaluated. For this, two convolutional neural networks used in various tasks of geospatial object extraction were trained (using the same hyperparameters) on a large dataset (containing aerial image data covering 8650 km2 of the Spanish territory that was labelled with binary road information) under twelve different scenarios, with each scenario featuring a different combination of tile size and overlap. To assess their generalisation capacity, the performance of all resulting models was evaluated on data from novel areas covering approximately 825 km2. The performance metrics obtained were analysed using appropriate descriptive and inferential statistical techniques to evaluate the impact of distinct levels of the fixed factors (tile size, tile overlap, and neural network architecture) on them. Statistical tests were applied to study the main and interaction effects of the fixed factors on the performance. A significance level of 0.05 was applied to all the null hypothesis tests. The results were highly significant for the main effects (p-values lower than 0.001), while the two-way and three-way interaction effects among them had different levels of significance. The results indicate that the training of road classification models on images with a higher tile size (more semantic context) and a higher amount of tile overlap (additional border context and continuity) significantly impacts their performance. The best model was trained on a dataset featuring tiles with a size of 1024 × 1024 pixels and a 12.5% overlap, and achieved a loss value of 0.0984, an F1 score of 0.8728, and an ROC-AUC score of 0.9766, together with an error rate of 3.5% on the test set.

Adjustment for sparse data bias in odds ratios: Significance to appraisal of risk of diabetes due to occupational trichlorfon insecticide exposure

BackgroundBias away from the null in odds ratios (OR), aggravated by low power, is a well-known phenomenon in statistics (sparse data bias). Such bias increases in presence of selection of “significant” results on the basis of null hypothesis testing (effect size magnification, ESM). ObjectivesWe seek to illustrate these issues and adjust for suspected sparse data bias in the context of a reported more than doubling of the odds of new onset type 2 diabetes in presence of occupational trichlorfon insecticide exposure reported in the Agricultural Health Study. MethodsWe performed ESM analysis on the crude ORs extracted from the contingency table in the published report, which is done by simulating selected OR given a posited true OR. Next, we applied easily accessible methods that adjust for sparse data bias to the extracted contingency tables, including data augmentation, bootstrap, Firth's regression, and Bayesian methods with weakly informative priors. ResultsDuring the ESM analysis, we observed that there was a reasonable chance that a “statistically significant” OR of around 2.5–2.6 would be observed for true OR of 1.2. Adjustment for sparse data bias revealed that Bayesian methods outperformed alternative approaches in terms of yielding more precise inference, while not making unjustified distributional assumptions about estimates of OR. The OR in the original paper of about 2.5–2.6 was reduced on average to OR of 1.9 to 2.2, with 95% (Bayesian) credible intervals that included the null. DiscussionIt is reasonable to adjust ORs for sparse data bias when the reported association has societal importance, because policy must be informed by the least biased estimates of the effect. We think that such adjustment would lead to a more appropriate evaluation of the extent of evidence on the contribution of occupational exposure to trichlorfon pesticide to risk of new onset diabetes.

Practical Bayesian Inference in Neuroscience: Or How I Learned to Stop Worrying and Embrace the Distribution.

Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in the replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian inference, representing a fundamentally different approach to hypothesis testing, is receiving renewed interest as a potential alternative or complement to traditional null significance hypothesis testing due to its ease of interpretation and explicit declarations of prior assumptions. Bayesian models are more mathematically complex than equivalent frequentist approaches, which have historically limited applications to simplified analysis cases. However, the advent of probability distribution sampling tools with exponential increases in computational power now allows for quick and robust inference under any distribution of data. Here we present a practical tutorial on the use of Bayesian inference in the context of neuroscientific studies in both rat electrophysiological and computational modeling data. We first start with an intuitive discussion of Bayes' rule and inference followed by the formulation of Bayesian-based regression and ANOVA models using data from a variety of neuroscientific studies. We show how Bayesian inference leads to easily interpretable analysis of data while providing an open-source toolbox to facilitate the use of Bayesian tools.

Minimal-Effect Testing, Equivalence Testing, and the Conventional Null Hypothesis Testing for the Analysis of Bi-Factor Models

A necessary step in applying bi-factor models is to evaluate the need for domain factors with a general factor in place. The conventional null hypothesis testing (NHT) was commonly used for such a purpose. However, the conventional NHT meets challenges when the domain loadings are weak or the sample size is insufficient. This article proposes using minimal-effect testing (MET) and equivalence testing (ET) to analyze bi-factor models. A key element in conducting MET and ET is the minimal size of factor loadings that can be regarded as noteworthy in practice, termed as minimal noteworthy size. This article presents two approaches to formulating the minimal noteworthy size and compares the pros and cons of MET, ET, and the conventional NHT. Analysis shows that MET, ET, and the conventional NHT are complementary. Combining them to test the noteworthiness of domain loadings can help researchers make a comprehensive judgment. Real and simulated datasets illustrate the applications of the three methods. Monte Carlo results show that MET and ET can control type I errors reasonably well while maintaining good statistical power.

Comparing researchers' degree of dichotomous thinking using frequentist versus Bayesian null hypothesis testing.

A large amount of scientific literature in social and behavioural sciences bases their conclusions on one or more hypothesis tests. As such, it is important to obtain more knowledge about how researchers in social and behavioural sciences interpret quantities that result from hypothesis test metrics, such as p-values and Bayes factors. In the present study, we explored the relationship between obtained statistical evidence and the degree of belief or confidence that there is a positive effect in the population of interest. In particular, we were interested in the existence of a so-called cliff effect: A qualitative drop in the degree of belief that there is a positive effect around certain threshold values of statistical evidence (e.g., at p = 0.05). We compared this relationship for p-values to the relationship for corresponding degrees of evidence quantified through Bayes factors, and we examined whether this relationship was affected by two different modes of presentation (in one mode the functional form of the relationship across values was implicit to the participant, whereas in the other mode it was explicit). We found evidence for a higher proportion of cliff effects in p-value conditions than in BF conditions (N = 139), but we did not get a clear indication whether presentation mode had an effect on the proportion of cliff effects. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 2 June 2023. The protocol, as accepted by the journal, can be found at: https://doi.org/10.17605/OSF.IO/5CW6P .

Nonparametric inference for NBUE distributions based on the TTT transform

This paper presents a uniformly consistent estimator for the cumulative distribution function, operating under the assumption of “new better than used in expectation” (NBUE). Additionally, it introduces a novel class of consistent tests for the NBUE null hypothesis. Both procedures leverage the empirical total time on test (TTT) transform. Finite sample properties of the proposed methods are investigated through simulations.

AN ANALYSIS OF THE LONG-TERM ASYMMETRIC RELATIONSHIP BETWEEN GOLD PRICES, EXCHANGE RATES, YIELDS OF GOVERNMENT BOND, AND THE BIST100 INDEX

This study examines the asymmetric effects of the gold price, the USD/TRY exchange rate, the corporate governance index, and the 10-year government bond yield on the BIST100 index, thus delving into the intricate web of financial markets. Using weekly data from September 2007 to September 2023, we employed the nonlinear ARDL model to examine the asymmetric effects of the variables on the BIST100 index. As a result, the nonlinear ARDL bound test proved the long-term cointegration between the BIST100 index and the explanatory variables used in the analysis. All asymmetric variables except the negative variable of gold price had statistically significant long-run effects on the BIST100 index. Government bond yields and gold price had negative effects on the stock index, while USD/TRY exchange rate and corporate governance index had positive effects on it. The Wald test's null hypothesis of no long-term asymmetries was rejected for government bond yields, the USD/TRY exchange rate, and the corporate governance index. On the other hand, the test for short-term asymmetry was rejected only for the corporate governance index. Accordingly, the corporate governance index has both short-term and long-term asymmetric effects on the BIST100 index, while government bond yields and the dollar exchange rate have only long-term asymmetric effects on it.

Inferring the allometric growth coefficient of juvenile African mud catfish, <i>Clarias gariepinus</i> (Burchell, 1822), using Bayesian and Frequentist regression models

Statistics is essential in biological and ecological scientific research. However, the default Frequentist statistics based on p-value and null hypothesis testing is often misused and misinterpreted, hence causing reproducible crises. The p-value concept deserved further examination because it has been irretrievably lost. Therefore, there is dire need for reform in the default Frequentist statistics as practiced by researchers because of the perils of p-values. Bayesian statistics, using the tools of Bayes Factors and posterior distributions derived from priors and likelihood function; rooted in Bayes’ Theorem is one of the suggested alternatives. Frequentist (least square) and Bayesian (specifying uniform Jeffreys-Zellner-Siow prior, r-scale =0.35) regression models, a standard statistical protocol in fisheries were applied to determine the allometric growth coefficient based on length (mm) and weight (g) measurements of juvenile African mud catfish, Clarias gariepinus from Epe Lagoon. The growth coefficient, b=3.20, 95% Confidence Interval [3.07, 3.34], t(96)=47.55, p<0.001 was significant with 96% explanatory power (R2=0.96).While Bayesian method, with 96% explanatory power (R2=0.96), also estimated, b=3.20, (with Credible Interval between 3.06 and 3.32). The Bayes Factor (>100) suggested the data is more plausible under the alternative model than the null model, but p-value cannot quantify evidence in support of alternative hypothesis, since p-value can only reject or fail to reject a null hypothesis. These findings suggested that juvenile C. gariepinus thrived in Epe Lagoon. Therefore, Bayesian inference is a robust substitute for Frequentist regression model in fisheries.

JWST/NIRCam Transmission Spectroscopy of the Nearby Sub-Earth GJ 341b

We present a JWST/Near Infrared Camera (NIRCam) transmission spectrum from 3.9 to 5.0 μm of the recently validated sub-Earth GJ 341b (R P = 0.92 R ⊕, T eq = 540 K) orbiting a nearby bright M1 star (d = 10.4 pc, K mag = 5.6). We use three independent pipelines to reduce the data from the three JWST visits and perform several tests to check for the significance of an atmosphere. Overall, our analysis does not uncover evidence of an atmosphere. Our null hypothesis tests find that none of our pipelines’ transmission spectra can rule out a flat line, although there is weak evidence for a Gaussian feature in two spectra from different pipelines (at 2.3 and 2.9σ). However, the candidate features are seen at different wavelengths (4.3 μm versus 4.7 μm), and our retrieval analysis finds that different gas species can explain these features in the two reductions (CO2 at 3.1σ compared to O3 at 2.9σ), suggesting that they are not real astrophysical signals. Our forward-model analysis rules out a low-mean-molecular-weight atmosphere (<350× solar metallicity) to at least 3σ, and disfavors CH4-dominated atmospheres at 1–3σ, depending on the reduction. Instead, the forward models find our transmission spectra are consistent with no atmosphere, a hazy atmosphere, or an atmosphere containing a species that does not have prominent molecular bands across the NIRCam/F444W bandpass, such as a water-dominated atmosphere. Our results demonstrate the unequivocal need for two or more transit observations analyzed with multiple reduction pipelines, alongside rigorous statistical tests, to determine the robustness of molecular detections for small exoplanet atmospheres.

Data aggregation can lead to biased inferences in Bayesian linear mixed models and Bayesian analysis of variance.

Bayesian linear mixed-effects models (LMMs) and Bayesian analysis of variance (ANOVA) are increasingly being used in the cognitive sciences to perform null hypothesis tests, where a null hypothesis that an effect is zero is compared with an alternative hypothesis that the effect exists and is different from zero. While software tools for Bayes factor null hypothesis tests are easily accessible, how to specify the data and the model correctly is often not clear. In Bayesian approaches, many authors use data aggregation at the by-subject level and estimate Bayes factors on aggregated data. Here, we use simulation-based calibration for model inference applied to several example experimental designs to demonstrate that, as with frequentist analysis, such null hypothesis tests on aggregated data can be problematic in Bayesian analysis. Specifically, when random slope variances differ (i.e., violated sphericity assumption), Bayes factors are too conservative for contrasts where the variance is small and they are too liberal for contrasts where the variance is large. Running Bayesian ANOVA on aggregated data can-if the sphericity assumption is violated-likewise lead to biased Bayes factor results. Moreover, Bayes factors for by-subject aggregated data are biased (too liberal) when random item slope variance is present but ignored in the analysis. These problems can be circumvented or reduced by running Bayesian LMMs on nonaggregated data such as on individual trials, and by explicitly modeling the full random effects structure. Reproducible code is available from https://osf.io/mjf47/. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A lineage perspective on hominin taxonomy and evolution.

An uncritical reliance on the phylogenetic species concept has led paleoanthropologists to become increasingly typological in their delimitation of new species in the hominin fossil record. As a practical matter, this approach identifies species as diagnosably distinct groups of fossils that share a unique suite of morphological characters but, ontologically, a species is a metapopulation lineage segment that extends from initial divergence to eventual extinction or subsequent speciation. Working from first principles of species concept theory, it is clear that a reliance on morphological diagnosabilty will systematically overestimate species diversity in the fossil record; because morphology can evolve within a lineage segment, it follows that early and late populations of the same species can be diagnosably distinct from each other. We suggest that a combination of morphology and chronology provides a more robust test of the single-species null hypothesis than morphology alone.