Statistically Research Articles

The Economic Impact on the Use of 100% Guide to All Common Entrance Examinations in Mathematics and Quantitative Reasoning: A Case Study of Delta State, Nigeria

This paper focuses on the financial implications and the families in Delta State, Nigeria who made use of Joshua Okwuwe’s “A 100% Guide to All Common Entrance Examinations in Mathematics and Quantitative Reasoning”. While there is a clear need for the book to be used in schools as a preparatory book focusing on preparing pupils for entrance examinations into secondary schools, this has added a very important changes which have had a positive impact on the financial achievements of a number of families especially those in the low income earners’ bracket. By using paper and online self-completed questionnaires to which interviews with parents, teachers and students were added after the surveys had been completed. The sample consists of 300 parents, 150 teachers, 500 students from Delta State who use the “100% Guide.” Questionnaires gathered information on the amount of money spent in the households on education materials, performance in mathematics/numeracy and views on the equity of education. Further, a combination of structured and semi structured interviews with school heads aimed at establishing their views on the use of the guide. For data analysis, the hypothesis test was done through the analysis of variance (ANOVA) to establish the statistical tests. Using statistical methodologies it is ascertained that the guide serves a purpose in raising the academic success by a 75% improvement in mathematics and 82% in quantitative reasoning for the learners but at the same time yields an average cost to the families. This economic stress is particularly felt by low income earners hence increasing learning inequity. These outcomes call for policy changes, including reduced-cost materials and the proper advertisement of cheap educational products. This study adds an important voice on the discourse on the pro and cons standardization of tests in Nigeria to the current academic writing based on its socioeconomic implications to students, educators, and policy makers in their strive towards enhancing learning for better performances and creating equity.

The Gewanci Application Media based on Smart Apps Creator on the Material of Imitating Animal Movements

In learning Cultural Arts in the realm of Dance, grade 1 teachers at SD Kanisius Beringin Semarang have not effectively utilized available learning resources; therefore, new media development is needed. The researcher aims to develop, determine the feasibility results, and test the effectiveness of the Smart Apps Creator-based Gewanci application media on the material of Imitating Animal Movements.This kind of study is referred to as development research using the ADDIE model. The research subjects consisted of media experts and material experts. The test was conducted on twenty three students who were in grade 1 of SD. This research use quantitative data to examine development. Data is collected via many methods such as observation, interviews, questionnaires, and testing. Data collection is conducted using a Likert Scale questionnaire. Data analysis encompasses quantitative, qualitative, and inferential statistical methodologies. The result of this research is to produce a learning media in the form of Gewanci Application which distinguishes itself from other applications, namely there are 15 videos of movement imitating rabbit animals, accompanied by attractive images and audio. This application is specifically designed to be in line with the teaching material. The findings of the media and material validation scores indicate that there are a total number of material validation scores with a percentage validation value of 93.7%. This suggests that these scores are very practical. Furthermore, media validation was considered very realistic, with a validation value percentage of 91%. Based on the t-test results, the use of the Gewanci application media has a significant impact. The N-gain test revealed a moderate improvement in learning outcomes from the pretest to the posttest. The findings suggest that the Gewanci app, developed using Smart Apps Creator, adequately meets the needs of dance education.

Data Mining for the Characterization of a Paper Prototype Obtained with Bacterial Cellulose Derived from Banana and Pineapple By-Products

The primary objective of this research is to evaluate the feasibility of two of the most prevalent agricultural residues in Ecuador, banana peels and pineapple peels, as a carbon source in the culture medium of Komagataeibacter hansenii for the production of bacterial cellulose (BC) and BC-based paper. This analysis includes an assessment of the productivity parameters of the obtained BC and the quality parameters of the BC-based paper, employing multivariate statistical methodologies. The experimental design consisted of fifteen treatments: T1 served as the control using the standard HS medium, while treatments T2–T8 used banana peel extracts (BPE), and treatments T9–T15 used pineapple peel extracts (PPE) at concentrations from 10% to 40% (v/v). Extracts were prepared with tailored pretreatments for each type of peel to optimize bioactive compound recovery. Standardized fermentation and purification conditions were applied, and once the cellulose was obtained, additives and coating agents were incorporated to produce paper samples from each treatment. The results indicated that higher BPE concentrations (T5, T6, T7, and T8) correlated significantly with increased Weight and Yield of BC, as well as improved grammage and water content in the BC-based paper. This highlights that efficient paper production is influenced by the quality of the bacterial cellulose used, with BPE-based media yielding optimal results due to their nutrient composition, which promotes bacterial growth and metabolic activity. This approach suggests a pathway for advancing sustainable and economical paper production.

The victims of the last great Bubonic plague epidemic in Ottoman Anatolia: dynamics of mortality by age and gender during the plague epidemic of Kastamonu (1836–1839)

ABSTRACT This study examines the bubonic plague epidemic that struck Kastamonu, a province in Northern Anatolia, between 1836 and 1839, offering a distinctive and thorough contribution to historical research on the plague within the framework of Ottoman studies. By employing rare daily death records, known as jurnal defteri, this research undertakes a quantitative analysis of mortality patterns during the epidemic—an approach rarely applied in Ottoman historical studies. The study investigates demographic details, analyzing the victims’ age and gender to uncover significant patterns, such as the particularly severe impact on younger age groups. Additionally, it compares mortality rates during the epidemic to those from non-plague-related deaths, providing a broader perspective on mortality dynamics and the overall demographic toll. This research bridges a critical gap in Ottoman plague studies by integrating statistical methodologies with demographic analysis. This approach helps to understand how such epidemics affected populations at a granular level. By focusing on an understudied region and time period, this study contributes novel insights into the social and demographic results of the plague in the Ottoman Empire. The findings underscore the epidemic’s profound impact on local communities, contributing to broader discussions on public health and mortality in historical contexts of Ottoman Empire..

Role of Statistics in Artificial Intelligence Technology

Artificial intelligence (AI) research and applications have sparked a broad scientific, economic, social, and political debate. Statistical approaches and techniques are fundamental to AI because they allow robots to learn from data and make intelligent decisions. It's possible even to view statistics as a fundamental component of AI. Statistics is an ideal partner for other disciplines in teaching, research, and practice because of its specialized knowledge of data evaluation, which begins with the correct phrasing of the research question and continues through a study design stage to analysis and interpretation of the results. This work aims to demonstrate how statistical methodology is relevant to the development of AI. In terms of methodological development, planning, research design, evaluation of data quality and collection, distinction of causation and relationships, and evaluation of result uncertainty, we talk about the contributions of statistics to the field of artificial intelligence. This study thoroughly analyzes the crucial role statistics play in AI, demonstrating the numerous applications of statistical ideas like probability, regression, classification, and clustering. The essay highlights the value of statistical analysis in handling uncertainty, making predictions, and training AI models all of which improve the efficiency and precision of AI systems.

Investigating Rainfall Patterns in South Singhbhum: A Non-parametric Approach Utilizing Mann-Kendall Tests and Sen's Slope Estimator

Aims: The South Singhbhum region is a physical region that experiences monsoon climate and frequent tropical cyclones and has global importance because of its abundant mineral resources like iron, manganese and its mining activities. The agricultural practices mainly depend on rainfall as it is a hilly area. For that reason, sustainable management is necessary to fulfill ecological, industrial, and community water requirements. So, the investigation carried out in this study focuses on the analysis of rainfall patterns within the South Singhbhum region by utilizing non-parametric statistical methodologies with the objective of identifying any discernible trends and variations that have occurred over time. Place and Duration of Study: South Singhbhum region of Odisha is extended between latitude to and longitude to . The area spread over on 45 blocks of Eight district in Odisha, India. The duration of time extended from 2008 to 2022. Methodology: The Mann-Kendall (MK) test is utilized to effectively detect the presence of monotonic trends within both the annual and seasonal rainfall datasets. Furthermore, the sequential Mann-Kendall (S-MK) test is employed to provide a comprehensive understanding of the temporal progression of these identified trends. The Sen's slope estimator is applied to accurately quantify the magnitude of the observed changes in rainfall patterns. Results: The annual study shows a steady declining trend from 2008 to 2021, with a Sen's Slope of 2.0376 and a Z value of 1.0949. However, March is the only month with a positive trend, as seen by its Sen's Slope of 3.0328 and Z value of 1.9708. There are significant variances in Sen's Slope values, which show disparities in the amount of changes, even if negative trends are common. The steepest negative slope is in July (-3.554), and the highest slope is in August (8.981). The S-MK Test was used to determine the start time of the South Singhbhum Region's annual rainfall time series, which revealed statistically declining patterns. It has been observed that the area's rainfall tendency varies frequently. In 2009, 2011, 2017, and 2019, the lines in the u(t) and u'(t) graphs intersect. The rainfall series experienced a reduction from 2008 to 2009, a rise from 2009 to 2011, a decrease from 2011 to 2017, another increase from 2017 to 2019, and a decline starting in 2019. Conclusion: The findings demonstrate that the South Singhbhum Region's rainfall patterns are trending negatively, which makes it clear that quick implementation of comprehensive water resource management methods is necessary to support sustainable development initiatives in the near future.

Misalignment of Biostatistics Content Between Licensing Exam Study Aids and Contemporary Medical Research

Background and Objectives: Medical trainees express difficulty with interpreting statistics in clinical literature. To elucidate educational gaps, we compared statistical methodologies in biomedical literature with biostatistical content in licensing exam study materials. Methods: In this bibliographic content analysis, we compiled a stratified random sample of articles involving original data analysis published during 2023 in 72 issues of three major medical journals. We recorded all discrete statistical methods and concepts detailed in the methods section of the articles and in three commercial licensing exam study resources. We created a unified list of discrete methods or concepts to define overarching domains and mapped each method to a domain to determine that domain’s presence in each resource or article. Results: In a sample of 273 journal articles and three study resources, we identified 1,057 unique key words mapped onto 20 domains. Statistical error, significance, power analysis, and group comparisons of categorical data were high-frequency domains among the articles. Overall, 63% of articles included methods from domains not covered in any study resource. Conclusions: Medical licensing exam preparation does not reflect the breadth of contemporary statistics in biomedical research. Future interventions should expand medical students’ understanding of research protocols and complex data manipulation.

Exploring critical animal-based traits as potential predictors of production diseases in dairy cattle: a systematic review and meta-analysis

This meta-analysis comprehensively evaluates the impact of production diseases on dairy cattle, examining a variety of physiological, metabolic, and behavioral traits. From an initial set of 3 349 records, a rigorous selection process resulted in 56 relevant articles, highlighting the strict criteria employed. The low inclusion rate of 1.67% underscores the dedication to studying quality and significance. Common reasons for exclusion, such as statistical methodology and the lack of a negative control group, emphasize the necessity for robust scientific investigation. The study reveals key indicators of production diseases, such as decreased milk production, weight loss, a low body condition score, digestive disorders, lameness, and respiratory distress. These findings align with existing knowledge, emphasizing the multifaceted nature of production diseases and their broad impact on dairy cattle health. Diseased animals consistently exhibit higher parameter values, supported by confidence intervals that indicate result precision. Subgroup analyses offer nuanced insights into the specific effects of diseases and interventions on different characteristics. Metritis and Mastitis significantly affect milk production, highlighting the economic implications for dairy farmers. Interventions such as improved milking techniques show promise. Similarly, interventions involving veterinary medications and controlled energy diets effectively mitigate weight loss, a low body condition score, and digestive disorders. The analysis underscores the vulnerability of dairy cattle to reproductive issues, metabolic imbalances, and increased susceptibility to infections. Interventions addressing these challenges include hormone therapy, balanced diets, and vaccination programs. Behavioral changes and feeding habits emerge as significant indicators of dairy cattle well-being. Ovarian cysts are identified as a major contributor to behavioral changes, necessitating targeted interventions. In conclusion, this meta-analysis provides valuable contributions to the field of dairy cattle health, offering specific insights into the effects of diseases and the effectiveness of interventions. These findings inform evidence-based practices for dairy stakeholders, ultimately enhancing animal welfare and promoting sustainable dairy production. However, establishing precise reference values presents challenges, warranting further investigation.

Analysis of factors influencing energy consumption of electric vehicles: Statistical, predictive, and causal perspectives

Analyzing the influence of various factors on the energy consumption of Electric Vehicles (EVs) yields a positive impact on mitigating range anxiety. Nevertheless, prevailing research predominantly relies on statistical or predictive methodologies to assess the influence of factors on energy consumption, offering limited causal insights. This study addresses this gap by utilizing the double/debiased machine learning (DML) combined with bootstrap-of-little-bags (BIB) inference approach to make causal inferences concerning the influence of factors on energy consumption. Furthermore, the study introduces a comprehensive framework that integrates statistical, predictive, and causal perspectives to examine the consistency and discrepancies in the influence of twelve factors on energy consumption. Notably, it unveils variations in the influence of these factors on energy consumption concerning different trip categories. Field datasets are utilized for framework validation, covering 20,385 valid trips from eight cities and four vehicle models. The findings demonstrate that DML can yield interpretable causal inferences, and the framework allows these three perspectives to complement each other, unveiling insights that remain concealed when using a single method. Furthermore, even for the same factor, there are variations in the influence on energy consumption across different trip categories. The proposed framework opens avenues for a comprehensive understanding of influencing factors on energy consumption of EVs, crucial for eco-driving and energy consumption predictions.

Utilization of Gamma & Weibull Distribution Functions in Validating Experimental-based Result of Ore Amenability for Coarse Gangue Rejection

Abstract The mining industry faces two-pronged challenges, that is the increasing demand for commodities and declining ore quality, resulting in higher energy consumption and operational costs but poor productivity. Multiple methods have been proposed to address this issue, including early coarse gangue rejection which requires further amenability testing by either experiments and statistical analysis. Experimental approaches, such as Gangue Rejection Amenability Tests (GRAT), have demonstrated efficacy but required substantial time and resources. Statistical methodologies utilizing lognormal distributions offered an alternative, but unable to validate statistical outcomes against experimental results. This study investigated the potential of alternative statistical methods (i.e., gamma and Weibull distributions) to validate GRAT. Through the analysis of grade and density parameters data drawn from eight ore fractions, this research constructed an accurate statistical model for predicting ore separation results. The model’s accuracy was evaluated based on coefficient of determination, heterogeneity of data, and comparison of statistical predictions of cumulative mass and metal yield with laboratory results. The results of employing two distributions did not yield accurate predictions of GRAT results. The statistical values tended to be higher than the experimental results for both parameters. We suggested that alternative functions be tested for more representative and suitable use in coarse gangue rejection applications.

IDiabetes platform—enhanced phenotyping of patients with diabetes for precision diagnosis, prognosis and treatment: study protocol for a cluster-randomised controlled study in Tayside, Scotland

Introduction and aimDiabetes is a global health emergency with increasing prevalence and diabetes-associated morbidity and mortality. One of the challenges in optimising diabetes care is translating research advances in this...

Deciphering cell to cell spatial relationship for pathology images using SpatialQPFs

Understanding spatial dynamics within tissue microenvironments is crucial for deciphering cellular interactions and molecular signaling in living systems. These spatial characteristics govern cell distribution, extracellular matrix components, and signaling molecules, influencing local biochemical and biophysical conditions. Despite significant progress in analyzing digital pathology images, current methods for capturing spatial relationships are limited. They often rely on specific spatial features that only partially describe the complex spatial distributions of cells and are frequently tied to particular outcomes within predefined model frameworks. Furthermore, these methods are typically limited to field of view analysis, which restricts their capacity to capture spatial patterns across whole-slide images, thereby limiting their ability to fully address the complexities of tissue architecture. To address these limitations, we present SpatialQPFs (Spatial Quantitative Pathology Features), an R package designed to extract interpretable spatial features from cell imaging data using spatial statistical methodologies. Leveraging segmented cell information, our package offers a comprehensive toolkit for applying a range of spatial statistical methods within a stochastic process framework, including analyses of point process data, areal data, and geostatistical data. By decoupling feature extraction from specific outcome models, SpatialQPFs enables thorough large-scale spatial analyses applicable across diverse clinical and biological contexts. This approach enhances the depth and accuracy of spatial insights derived from tissue data, empowering researchers to conduct comprehensive spatial analyses efficiently and reproducibly. By providing a flexible and robust framework for spatial feature extraction, SpatialQPFs facilitates advanced spatial analyses, paving the way for new discoveries in tissue biology and pathology. SpatialQPFs code and documentation are publicly available at https://github.com/Genentech/SpatialQPFs.

From Remittance to E-Commerce: How Electronic Wallets Change the Way Indians Transact

In today's digital realm, electronic wallets (E-wallets) have seamlessly integrated into our daily transactions, offering unparalleled convenience and efficiency to users worldwide. This research paper aims to delve into the impact of various E-wallets, including well-known platforms like Paytm and Google Pay, on consumer preferences. Through an extensive study, approximately 250 individuals were surveyed to discern their E-wallet preferences and the factors steering their decisions. Leveraging statistical methodologies such as 2-sample T-tests and graphical analysis, this research investigates the relationship between independent variables and E-wallet adoption. Initial findings highlight the crucial role of factors such as user experience, security features, and brand reputation in shaping consumer preferences for E-wallets. This study contributes significantly to our understanding of consumer behaviour in the digital payment ecosystem, offering valuable insights into the determinants influencing E-wallet usage across diverse demographics.

TestCompareR: an R package to compare two binary diagnostic tests using paired data

Background Binary diagnostic tests are commonly used in medicine to answer a question about a patient’s clinical status, most commonly, do they or do they not have some disease. Recent advances in statistical methodologies for performing inferential statistics to compare commonly used test metrics for two diagnostic tests have not yet been implemented in a statistical package. Methods Up-to-date statistical methods to compare the test metrics achieved by two binary diagnostic tests are implemented in the new R package testCompareR. The output and efficiency of testCompareR is compared to the only other available package which performs this function, DTComPair, as well as an open-source program, compbdt, using a motivating example. Results testCompareR achieves similar results to DTComPair using statistical methods with improved coverage and asymptotic performance. Further, testCompareR is faster than the currently available package and requires fewer pre-processing steps in order to produce accurate results. Conclusions testCompareR provides a new tool to compare the test metrics for two binary diagnostic tests compared with the gold standard. This tool allows flexible inputs, which minimises the need for data pre-processing, and operates in very few steps, so that it is easy to use even for those less experienced with R. testCompareR achieves results comparable to those computed by DTComPair, using optimised statistical methods and with improved computational efficiency.

On Bayes factors for hypothesis tests.

We develop alternative families of Bayes factors for use in hypothesis tests as alternatives to the popular default Bayes factors. The alternative Bayes factors are derived for the statistical analyses most commonly used in psychological research - one-sample and two-sample ttests, regression, and ANOVA analyses. They possess the same desirable theoretical and practical properties as the default Bayes factors and satisfy additional theoretical desiderata while mitigating against two features of the default priors that we consider implausible. They can be conveniently computed via an R package that we provide. Furthermore, hypothesis tests based on Bayes factors and those based on significance tests are juxtaposed. This discussion leads to the insight that default Bayes factors as well as the alternative Bayes factors are equivalent to test-statistic-based Bayes factors as proposed by Johnson. Journal of the Royal Statistical Society Series B: Statistical Methodology, 67, 689-701. (2005). We highlight test-statistic-based Bayes factors as a general approach to Bayes-factor computation that is applicable to many hypothesis-testing problems for which an effect-size measure has been proposed and for which test power can be computed.

The 2009 FDA PRO guidance, Potential Type I error, Descriptive Statistics and Pragmatic estimation of the number of interviews for item elicitation.

A statistical methodology named "capture recapture", a Kaplan-Meier Summary Statistic, and an urn model framework are presented to describe the elicitation, then estimate both the number of interviews and the total number of items ("codes") that will be elicited during patient interviews, and present a summary graphical statistic that "saturation" has occurred. This methodology is developed to address a gap in the FDA 2009 PRO and 2012 PFDD guidance for determining the number of interviews (sample size). This estimate of the number of interviews (sample size) uses a two-step procedure. The estimate of the total number of items is then used to estimate the number of interviews to elicit all items. A framework called an urn model is a framework for describing the elicitation and demonstrate the algorithm for declaring saturation "first interview with zero new codes". A caveat emptor is that due to independence assumptions, the urn model is not used as a method for estimating probabilities. The URN model provides a framework to demonstrate that an algorithm such as "first interview with zero new codes" may establish that all codes have been elicited. The limitations of the Urn model, capture recapture, and Kaplan-Meier are summarized. The statistical methods and the estimates supplement but do not replace expert judgement and declaration of "saturation." A graphical summary statistic is presented to summarize "saturation," after expert declaration for two algorithms. An example of a capture-recapture estimate, using simulated data is provided. The example suggests that the estimate of total number of codes may be accurate when prepared as early as the second interview. A second simulation is presented with an URN model, under a strong assumption of independence that an algorithm such as 'first interview with zero new codes" may fail to identify all codes. Potential errors in declaration of saturation are presented. Recommendations are presented for additional research and the use of the algorithm "first interview with zero new codes."

Artificial intelligence in pharmacogenetics: A narrative review of current and future applications

Pharmacogenetics aims to investigate the correlation between patient genetic characteristics and the efficacy of pharmaceutical agents, while concurrently evaluating the risks of adverse reactions. This field of research necessitates the application of complex statistical analysis methodologies, and artificial intelligence (AI) capabilities are increasingly being leveraged for such analyses. AI represents an advanced technology employed to automate the execution of tasks that traditionally demand substantial human intellectual effort. A review of scientific literature on the application of machine learning models in pharmacogenetic research has demonstrated that AI is a highly sophisticated and flexible tool capable of facilitating the widespread implementation of pharmacogenetics in clinical practice. A promising area for the application of AI in pharmacogenetics involves the integration of this technology into tasks related to the analysis, detection, prediction, and support of pharmacogenetic information and decision-making systems. The utilization of deep learning technologies has the potential to expand the understanding of drug pharmacodynamics, indications, and contraindications, which may potentially lead to the updating of educational and methodological literature on pharmacology and substantially advance the quality of patient pharmacotherapy. However, the implementation of AI technologies may be hindered by factors such as a shortage of qualified personnel, ethical disagreements, and complexities in legal regulation of this domain. Nonetheless, the application of AI technologies in pharmacogenetic research demonstrates high effectiveness and expediency, despite the existing challenges.

Some theoretical foundations for the design and analysis of randomized experiments

Abstract Neyman’s seminal work in 1923 has been a milestone in statistics over the century, which has motivated many fundamental statistical concepts and methodology. In this review, we delve into Neyman’s groundbreaking contribution and offer technical insights into the design and analysis of randomized experiments. We shall review the basic setup of completely randomized experiments and the classical approaches for inferring the average treatment effects. We shall, in particular, review more efficient design and analysis of randomized experiments by utilizing pretreatment covariates, which move beyond Neyman’s original work without involving any covariate. We then summarize several technical ingredients regarding randomizations and permutations that have been developed over the century, such as permutational central limit theorems and Berry–Esseen bounds, and we elaborate on how these technical results facilitate the understanding of randomized experiments. The discussion is also extended to other randomized experiments including rerandomization, stratified randomized experiments, matched pair experiments, and cluster randomized experiments.

Exploring statistical physics principles for superior Pefloxacin extraction from water via halloysite nanotubes: Stereographic and topographic evaluation

This novel research incorporates statistical physics methodologies to microscopically scrutinize and interpret the retention dynamics of Pefloxacin from aqueous solutions via Halloysite Nanotubes (HNTs) for water detoxification. Measurement points were matched under the scope of four distinct statistical isotherm frameworks: the single-energy monolayer, dual-energy monolayer, tri-energetic monolayer, and dual-energy bilayer. An error quantification approach, incorporating R2, reduced Chi-Square, RSS, and R2adj, identified the dual-energy bilayer as the most suitable model. Rooted in the more realistic scenario, stereographic, energetic and thermodynamic parameters in addition to the pore size distribution (PSD) and adhesion energy distribution (AED) functions have been successfully developed and applied to examine the intricate details of the docking mechanisms. Stereographic examination unveiled that the linking cavities preferentially capture a majority of the attached species with n > 1 at specific temperatures indicating a specific-anchorage mechanism with substantial aggregation. Statistical thermodynamic exploration evidenced that the retention operation in this study is entropy-driven, and endothermic. Combining temperature and concentration impacts gives entropy a more complex behavior. Finally, the PSD and AED assessments revealed the mesoporous geometric size and energetic surface heterogeneity confirming the endothermic nature and physisorption-dominated mechanism (<25 kJ/mol) of the Pefloxacin/HNTs area-anchoring.

An open letter to evolutionary and human sciences; Statistics has moved on and so should we. A proposal for more transparent research, and some notes on p < 0.003

Statistical reasoning and inference have become integral to how we reach conclusions as scientists. Few papers in evolutionary, biological, and human sciences, are published at present without at least one report of a p-value, or the term “statistically significant”. As a product of this, discoveries and results often hinge on p-values below the infamous threshold of 0.05. This is due to how well ingrained these notions are in many of our higher education systems, and our practices as researchers. Nevertheless, the concept of a p-value, borrowed from statistics, and dating as far back as the late 20th and early 21st century, has undergone an evolution that we may not be completely aware of, if we do not follow or keep ourselves up to date with the most recent advances in statistical research. The present short communication can be framed as an opinion piece that simply aims to call our attention to the fact that statisticians, in recent years, have asked that we completely abandon the notion of “statistically significant (p < 0.05)”. It is important to point out that none of these observations are new, however certainly merit being re-addressed. Here I attempt to open a dialogue among archaeologists, palaeontologists, palaeoanthropologists, biological anthropologists, evolutionary biologists, and all researchers in related fields, about such statistical research. Finally, I propose a means of moving forward, suggesting we move from a world where p < 0.05 is considered a binary threshold for conclusive results, to a more nuanced practice where different gradients, down to p < 0.003, are interpreted as increasingly suggestive evidence – provided that findings are supported by additional, transparently reported corroborative data.