Fundamental concepts in statistics: elucidation and illustration

Assessment of climate change induced rainfall trend and variability with non-parametric and linear approach for Sirajganj district, Bangladesh

The monthly and annual trends and variance of rainfall have been studied for five stations in an economically important Bangladeshi district named Sirajganj since 1965 to 2021. Natural disasters have prevalent in Sirajganj which is indispensable to assess. But, several researchers have been normally focused on river bank management and flood risk assessment. However, no extensive research has been conducted on Sirajganj based on non-normally distributed time series meteorological data such as rainfall time series so the current study is very important. In this study, the non-parametric Mann-Kendall and Sen's methods have been used to determine the statistical significance of a positive or negative trend in rainfall data. Also, cumulative sum charts and bootstrapping, one-way ANOVA, Tukey's range tests, and linear regression have been used to discover the incidence of abrupt changes, compare the significant difference in monthly and annual rainfall data, multiple comparisons amidst mentioned stations to find changes, and to investigate the changeover on dry and rainy days, respectively. The analysis showed a statistically significant decreasing trends in monthly and annual rainfall series. As well, changes from positive to negative direction have been recognized in the February, May, July, September, and annual rainfall time sequence. Besides, ANOVA and Tukey's range tests revealed a statistically substantial difference in all monthly and annual rainfall volume excluding January, March, and June. Additionally, these two tests demonstrated momentous differences in all monthly and annual frequency of rainfall categories excepting January and April. However, Linear regression analysis revealed that the number of dry days gradually reduced at the end of the dry winter, though the number of rainy days decreased during the rainy season. As in, the number of rainy days replaces the number of dry days during the dry season and vice versa during the rainy season. Even though, with very few exceptions, the volume of rainfall decreases throughout the year. The outcomes of this research might helpful for implementing the planning and evaluating hydrological projects on Sirajganj district.

Effects of Telephone-Based Brief Motivational Interviewing on Self-Management, Medication Adherence, and Glycemic Control in Patients with Uncontrolled Type 2 Diabetes Mellitus in a Rural Community in Thailand

Introduction

Owing to the increased prevalence of type 2 diabetes mellitus (T2DM) and the high proportion of patients with uncontrolled T2DM, effective interventions for disease management are needed.

Objective

The study aim was to test the effects of brief motivational interviewing (MI) on patients' self-management, medication adherence, and glycemic control.

Methods

A single-group repeated measures trial was used to examine the effects of usual care only and usual care plus telephone-based brief MI. Participants were 29 patients with uncontrolled T2DM recruited from a rural primary care setting in Nakhon Sawan, Thailand. Participants received usual care during the first 4 weeks, followed by usual care plus brief MI during weeks 4-8. Outcomes of self-management, medication adherence, fasting blood sugar (FBS) levels, and hemoglobin A1c (HbA1c) levels were assessed at baseline, 4 weeks, and 8 weeks. Data were analyzed using descriptive statistics, one-way repeated measures analysis of variance, and Friedman test.

Results

Significant changes in self-management (p < 0.001), medication adherence (p < 0.001), and FBS (p < 0.05) were observed over the 8-week study. In multiple comparisons, self-management was the only parameter significantly different across baseline, 4, and 8 weeks (p <0.05, <0.001, and <0.001, respectively); medication adherence was significantly different between 4 and 8 weeks (p < 0.05), and between baseline and 8 weeks (p < 0.001); and FBS was significantly different between 4 and 8 weeks (p < 0.05). HbA1c declined over the 8-week study, but not significantly.

Conclusion

An intervention combining telephone-based brief MI with usual care significantly increased self-management, medication adherence, and glycemic control (ie, FBS) after 4 weeks, whereas usual care only significantly increased self-management. Phone-based brief MI may be an effective way for healthcare providers to remotely enhance patients' self-management and glycemic control, thus reducing barriers related to time and geographic location.

Physical activity-related quality of life in breast cancer survivors compared to healthy women

OBJECTIVE

To evaluate physical activity-related quality of life (PAQOL) in breast cancer survivors compared to healthy women.

METHODS

Physical activity level was measured as not active, somewhat active, active or very active. Intensity was reported as hours per week of light, moderate and vigorous activity. Physical activity-related quality of life was measured with the Vitality Plus Scale, a self-report instrument developed and validated to measure exercise-related health benefits.

RESULTS

Compared to healthy women (n = 23), breast cancer survivors (n = 23) were older (57.0 ± 2.3 vs. 49.0 ± 1.1 years; p < .01) and reported more light activity (4.1 ± 0.6 vs. 2.4 ± 0.5 hr/week; p < .05), but no differences in PAQOL. However, when grouped by physical activity level there were no differences in age, but inactive women (n = 30) had higher body mass index (29.3 ± 1.0 vs. 25.1 ± 1.1 kg/m² ; p < .05) and reported less moderate (1.3 ± 0.4 vs. 3.6 ± 0.8 hr/week; p < .05) and vigorous (0.2 ± 0.1 vs. 1.9 ± 0.5 hr/week; p < .01) activity than active women (n = 16). Furthermore, active women reported higher overall PAQOL, greater energy levels and quicker sleep onset than inactive women (p < .05).

CONCLUSIONS

In these women, moderate and vigorous physical activity and PAQOL were not influenced by breast cancer survivorship. Despite diagnosis, active women who engaged in greater amounts of moderate and vigorous activity reported better PAQOL than inactive women.

Matrix Polysaccharides and SiaD Diguanylate Cyclase Alter Community Structure and Competitiveness of Pseudomonas aeruginosa during Dual-Species Biofilm Development with Staphylococcus aureus

Bacteria in natural and engineered environments form biofilms that include many different species. Microorganisms rely on a number of different strategies to manage social interactions with other species and to access resources, build biofilm consortia, and optimize growth. For example, Pseudomonas aeruginosa and Staphylococcus aureus are biofilm-forming bacteria that coinfect the lungs of cystic fibrosis patients and diabetic and chronic wounds. P. aeruginosa is known to antagonize S. aureus growth. However, many of the factors responsible for mixed-species interactions and outcomes such as infections are poorly understood. Biofilm bacteria are encased in a self-produced extracellular matrix that facilitates interspecies behavior and biofilm development. In this study, we examined the poorly understood roles of the major matrix biopolymers and their regulators in mixed-species biofilm interactions and development.

Mixed-species biofilms display a number of emergent properties, including enhanced antimicrobial tolerance and communal metabolism. These properties may depend on interspecies relationships and the structure of the biofilm. However, the contribution of specific matrix components to emergent properties of mixed-species biofilms remains poorly understood. Using a dual-species biofilm community formed by the opportunistic pathogens Pseudomonas aeruginosa and Staphylococcus aureus, we found that whilst neither Pel nor Psl polysaccharides, produced by P. aeruginosa, affect relative species abundance in mature P. aeruginosa and S. aureus biofilms, Psl production is associated with increased P. aeruginosa abundance and reduced S. aureus aggregation in the early stages of biofilm formation. Our data suggest that the competitive effect of Psl is not associated with its structural role in cross-linking the matrix and adhering to P. aeruginosa cells but is instead mediated through the activation of the diguanylate cyclase SiaD. This regulatory control was also found to be independent of the siderophore pyoverdine and Pseudomonas quinolone signal, which have previously been proposed to reduce S. aureus viability by inducing lactic acid fermentation-based growth. In contrast to the effect mediated by Psl, Pel reduced the effective crosslinking of the biofilm matrix and facilitated superdiffusivity in microcolony regions. These changes in matrix cross-linking enhance biofilm surface spreading and expansion of microcolonies in the later stages of biofilm development, improving overall dual-species biofilm growth and increasing biovolume severalfold. Thus, the biofilm matrix and regulators associated with matrix production play essential roles in mixed-species biofilm interactions.

Poor statistical reporting, inadequate data presentation and spin persist despite editorial advice

The Journal of Physiology and British Journal of Pharmacology jointly published an editorial series in 2011 to improve standards in statistical reporting and data analysis. It is not known whether reporting practices changed in response to the editorial advice. We conducted a cross-sectional analysis of reporting practices in a random sample of research papers published in these journals before (n = 202) and after (n = 199) publication of the editorial advice. Descriptive data are presented. There was no evidence that reporting practices improved following publication of the editorial advice. Overall, 76-84% of papers with written measures that summarized data variability used standard errors of the mean, and 90-96% of papers did not report exact p-values for primary analyses and post-hoc tests. 76-84% of papers that plotted measures to summarize data variability used standard errors of the mean, and only 2-4% of papers plotted raw data used to calculate variability. Of papers that reported p-values between 0.05 and 0.1, 56-63% interpreted these as trends or statistically significant. Implied or gross spin was noted incidentally in papers before (n = 10) and after (n = 9) the editorial advice was published. Overall, poor statistical reporting, inadequate data presentation and spin were present before and after the editorial advice was published. While the scientific community continues to implement strategies for improving reporting practices, our results indicate stronger incentives or enforcements are needed.

Statistical considerations in reporting cardiovascular research

The problem of inadequate statistical reporting is long standing and widespread in the biomedical literature, including in cardiovascular physiology. Although guidelines for reporting statistics have been available in clinical medicine for some time, there are currently no guidelines specific to cardiovascular physiology. To assess the need for guidelines, we determined the type and frequency of statistical tests and procedures currently used in the American Journal of Physiology-Heart and Circulatory Physiology. A PubMed search for articles published in the American Journal of Physiology-Heart and Circulatory Physiology between January 1, 2017, and October 6, 2017, provided a final sample of 146 articles evaluated for methods used and 38 articles for indepth analysis. The t-test and ANOVA accounted for 71% (212 of 300 articles) of the statistical tests performed. Of six categories of post hoc tests, Bonferroni and Tukey tests were used in 63% (62 of 98 articles). There was an overall lack in details provided by authors publishing in the American Journal of Physiology-Heart and Circulatory Physiology, and we compiled a list of recommended minimum reporting guidelines to aid authors in preparing manuscripts. Following these guidelines could substantially improve the quality of statistical reports and enhance data rigor and reproducibility.

Explorations in statistics: the log transformation

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This thirteenth installment of Explorations in Statistics explores the log transformation, an established technique that rescales the actual observations from an experiment so that the assumptions of some statistical analysis are better met. A general assumption in statistics is that the variability of some response Y is homogeneous across groups or across some predictor variable X. If the variability-the standard deviation-varies in rough proportion to the mean value of Y, a log transformation can equalize the standard deviations. Moreover, if the actual observations from an experiment conform to a skewed distribution, then a log transformation can make the theoretical distribution of the sample mean more consistent with a normal distribution. This is important: the results of a one-sample t test are meaningful only if the theoretical distribution of the sample mean is roughly normal. If we log-transform our observations, then we want to confirm the transformation was useful. We can do this if we use the Box-Cox method, if we bootstrap the sample mean and the statistic t itself, and if we assess the residual plots from the statistical model of the actual and transformed sample observations.

The thrill of the paper, the agony of the review

The process of first writing a scientific paper and then responding to reviewer comments can be challenging and sometimes-some might say often-frustrating. In this personal view, I recount some of my experiences as an author, and I offer some strategies to write a paper and to then respond to comments from the people who reviewed it.

Explorations in statistics: the assumption of normality

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This twelfth installment of Explorations in Statistics explores the assumption of normality, an assumption essential to the meaningful interpretation of a t test. Although the data themselves can be consistent with a normal distribution, they need not be. Instead, it is the theoretical distribution of the sample mean or the theoretical distribution of the difference between sample means that must be roughly normal. The most versatile approach to assess normality is to bootstrap the sample mean, the difference between sample means, or t itself. We can then assess whether the distributions of these bootstrap statistics are consistent with a normal distribution by studying their normal quantile plots. If we suspect that an inference we make from a t test may not be justified-if we suspect that the theoretical distribution of the sample mean or the theoretical distribution of the difference between sample means is not normal-then we can use a permutation method to analyze our data.

CORP: Minimizing the chances of false positives and false negatives

Statistics is essential to the process of scientific discovery. An inescapable tenet of statistics, however, is the notion of uncertainty which has reared its head within the arena of reproducibility of research. The Journal of Applied Physiology’s recent initiative, “Cores of Reproducibility in Physiology,” is designed to improve the reproducibility of research: each article is designed to elucidate the principles and nuances of using some piece of scientific equipment or some experimental technique so that other researchers can obtain reproducible results. But other researchers can use some piece of equipment or some technique with expert skill and still fail to replicate an experimental result if they neglect to consider the fundamental concepts of statistics of hypothesis testing and estimation and their inescapable connection to the reproducibility of research. If we want to improve the reproducibility of our research, then we want to minimize the chance that we get a false positive and—at the same time—we want to minimize the chance that we get a false negative. In this review I outline strategies to accomplish each of these things. These strategies are related intimately to fundamental concepts of statistics and the inherent uncertainty embedded in them.

Explorations in statistics: statistical facets of reproducibility

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This eleventh installment of Explorations in Statistics explores statistical facets of reproducibility. If we obtain an experimental result that is scientifically meaningful and statistically unusual, we would like to know that our result reflects a general biological phenomenon that another researcher could reproduce if (s)he repeated our experiment. But more often than not, we may learn this researcher cannot replicate our result. The National Institutes of Health and the Federation of American Societies for Experimental Biology have created training modules and outlined strategies to help improve the reproducibility of research. These particular approaches are necessary, but they are not sufficient. The principles of hypothesis testing and estimation are inherent to the notion of reproducibility in science. If we want to improve the reproducibility of our research, then we need to rethink how we apply fundamental concepts of statistics to our science.

Standard deviation and standard error of the mean

In most clinical and experimental studies, the standard deviation (SD) and the estimated standard error of the mean (SEM) are used to present the characteristics of sample data and to explain statistical analysis results. However, some authors occasionally muddle the distinctive usage between the SD and SEM in medical literature. Because the process of calculating the SD and SEM includes different statistical inferences, each of them has its own meaning. SD is the dispersion of data in a normal distribution. In other words, SD indicates how accurately the mean represents sample data. However the meaning of SEM includes statistical inference based on the sampling distribution. SEM is the SD of the theoretical distribution of the sample means (the sampling distribution). While either SD or SEM can be applied to describe data and statistical results, one should be aware of reasonable methods with which to use SD and SEM. We aim to elucidate the distinctions between SD and SEM and to provide proper usage guidelines for both, which summarize data and describe statistical results.

A hypothesis to explain childhood cancers near nuclear power plants

Over 60 epidemiological studies world-wide have examined cancer incidences in children near nuclear power plants (NPPs): most of them indicate leukemia increases. These include the 2008 KiKK study commissioned by the German Government which found relative risks (RR) of 1.6 in total cancers and 2.2 in leukemias among infants living within 5 km of all German NPPs. The KiKK study has retriggered the debate as to the cause(s) of these increased cancers. A suggested hypothesis is that the increased cancers arise from radiation exposures to pregnant women near NPPs. However any theory has to account for the >10,000 fold discrepancy between official dose estimates from NPP emissions and observed increased risks. An explanation may be that doses from spikes in NPP radionuclide emissions are significantly larger than those estimated by official models which are diluted through the use of annual averages. In addition, risks to embryos/fetuses are greater than those to adults and haematopoietic tissues appear more radiosensitive in embryos/fetuses than in newborn babies. The product of possible increased doses and possible increased risks per dose may provide an explanation.

Consequences of common data analysis inaccuracies in CNS trauma injury basic research

The development of successful treatments for humans after traumatic brain or spinal cord injuries (TBI and SCI, respectively) requires animal research. This effort can be hampered when promising experimental results cannot be replicated because of incorrect data analysis procedures. To identify and hopefully avoid these errors in future studies, the articles in seven journals with the highest number of basic science central nervous system TBI and SCI animal research studies published in 2010 (N=125 articles) were reviewed for their data analysis procedures. After identifying the most common statistical errors, the implications of those findings were demonstrated by reanalyzing previously published data from our laboratories using the identified inappropriate statistical procedures, then comparing the two sets of results. Overall, 70% of the articles contained at least one type of inappropriate statistical procedure. The highest percentage involved incorrect post hoc t-tests (56.4%), followed by inappropriate parametric statistics (analysis of variance and t-test; 37.6%). Repeated Measures analysis was inappropriately missing in 52.0% of all articles and, among those with behavioral assessments, 58% were analyzed incorrectly. Reanalysis of our published data using the most common inappropriate statistical procedures resulted in a 14.1% average increase in significant effects compared to the original results. Specifically, an increase of 15.5% occurred with Independent t-tests and 11.1% after incorrect post hoc t-tests. Utilizing proper statistical procedures can allow more-definitive conclusions, facilitate replicability of research results, and enable more accurate translation of those results to the clinic.

What to use to express the variability of data: Standard deviation or standard error of mean?

Statistics plays a vital role in biomedical research. It helps present data precisely and draws the meaningful conclusions. While presenting data, one should be aware of using adequate statistical measures. In biomedical journals, Standard Error of Mean (SEM) and Standard Deviation (SD) are used interchangeably to express the variability; though they measure different parameters. SEM quantifies uncertainty in estimate of the mean whereas SD indicates dispersion of the data from mean. As readers are generally interested in knowing the variability within sample, descriptive data should be precisely summarized with SD. Use of SEM should be limited to compute CI which measures the precision of population estimate. Journals can avoid such errors by requiring authors to adhere to their guidelines.

Explorations in statistics: permutation methods

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This eighth installment of Explorations in Statistics explores permutation methods, empiric procedures we can use to assess an experimental result-to test a null hypothesis-when we are reluctant to trust statistical theory alone. Permutation methods operate on the observations-the data-we get from an experiment. A permutation procedure answers this question: out of all the possible ways we can rearrange the observations we got, in what proportion of those arrangements is the sample statistic we care about at least as extreme as the one we got? The answer to that question is the P value.

Explorations in statistics: regression

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This seventh installment of Explorations in Statistics explores regression, a technique that estimates the nature of the relationship between two things for which we may only surmise a mechanistic or predictive connection. Regression helps us answer three questions: does some variable Y depend on another variable X; if so, what is the nature of the relationship between Y and X; and for some value of X, what value of Y do we predict? Residual plots are an essential component of a thorough regression analysis: they help us decide if our statistical regression model of the relationship between Y and X is appropriate.

Explorations in statistics: correlation

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This sixth installment of Explorations in Statistics explores correlation, a familiar technique that estimates the magnitude of a straight-line relationship between two variables. Correlation is meaningful only when the two variables are true random variables: for example, if we restrict in some way the variability of one variable, then the magnitude of the correlation will decrease. Correlation cannot help us decide if changes in one variable result in changes in the second variable, if changes in the second variable result in changes in the first variable, or if changes in a third variable result in concurrent changes in the first two variables. Correlation can help provide us with evidence that study of the nature of the relationship between x and y may be warranted in an actual experiment in which one of them is controlled.

Explorations in statistics: power

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This fifth installment of Explorations in Statistics revisits power, a concept fundamental to the test of a null hypothesis. Power is the probability that we reject the null hypothesis when it is false. Four things affect power: the probability with which we are willing to reject-by mistake-a true null hypothesis, the magnitude of the difference we want to be able to detect, the variability of the underlying population, and the number of observations in our sample. In an application to an Institutional Animal Care and Use Committee or to the National Institutes of Health, we define power to justify the sample size we propose.

The null hypothesis significance test in health sciences research (1995-2006): statistical analysis and interpretation

BACKGROUND

The null hypothesis significance test (NHST) is the most frequently used statistical method, although its inferential validity has been widely criticized since its introduction. In 1988, the International Committee of Medical Journal Editors (ICMJE) warned against sole reliance on NHST to substantiate study conclusions and suggested supplementary use of confidence intervals (CI). Our objective was to evaluate the extent and quality in the use of NHST and CI, both in English and Spanish language biomedical publications between 1995 and 2006, taking into account the International Committee of Medical Journal Editors recommendations, with particular focus on the accuracy of the interpretation of statistical significance and the validity of conclusions.

METHODS

Original articles published in three English and three Spanish biomedical journals in three fields (General Medicine, Clinical Specialties and Epidemiology - Public Health) were considered for this study. Papers published in 1995-1996, 2000-2001, and 2005-2006 were selected through a systematic sampling method. After excluding the purely descriptive and theoretical articles, analytic studies were evaluated for their use of NHST with P-values and/or CI for interpretation of statistical "significance" and "relevance" in study conclusions.

RESULTS

Among 1,043 original papers, 874 were selected for detailed review. The exclusive use of P-values was less frequent in English language publications as well as in Public Health journals; overall such use decreased from 41% in 1995-1996 to 21% in 2005-2006. While the use of CI increased over time, the "significance fallacy" (to equate statistical and substantive significance) appeared very often, mainly in journals devoted to clinical specialties (81%). In papers originally written in English and Spanish, 15% and 10%, respectively, mentioned statistical significance in their conclusions.

CONCLUSIONS

Overall, results of our review show some improvements in statistical management of statistical results, but further efforts by scholars and journal editors are clearly required to move the communication toward ICMJE advices, especially in the clinical setting, which seems to be imperative among publications in Spanish.

Childhood cancer near German nuclear power stations

In 2008, the Kinderkrebs in der Umgebung von Kernkraftwerken (KiKK) study in Germany reported a 60% increase in solid cancers and a 120% increase in leukemias among children living within 5 km of all German nuclear power stations. The study has triggered debates as to the cause(s) of these increased cancers. This article reports on the findings of the KiKK study; discusses past and more recent epidemiological studies of leukemias near nuclear installations around the world, and outlines a possible biological mechanism to explain the increased cancers. This suggests that the observed high rates of infant leukemias may be a teratogenic effect from radionuclides incorporated by pregnant women living near nuclear reactors. Doses and risks from environmental emissions to embryos and fetuses may be larger than suspected. Hematopoietic tissues appear to be considerably more radiosensitive in embryos/fetuses than in newborn babies. Recommendations for advice to local residents and for further research are made.

Explorations in statistics: the bootstrap

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This fourth installment of Explorations in Statistics explores the bootstrap. The bootstrap gives us an empirical approach to estimate the theoretical variability among possible values of a sample statistic such as the sample mean. The appeal of the bootstrap is that we can use it to make an inference about some experimental result when the statistical theory is uncertain or even unknown. We can also use the bootstrap to assess how well the statistical theory holds: that is, whether an inference we make from a hypothesis test or confidence interval is justified.

Commentary: childhood cancer near nuclear power stations

In 2008, the KiKK study in Germany reported a 1.6-fold increase in solid cancers and a 2.2-fold increase in leukemias among children living within 5 km of all German nuclear power stations. The study has triggered debates as to the cause(s) of these increased cancers. This article reports on the findings of the KiKK study; discusses past and more recent epidemiological studies of leukemias near nuclear installations around the world, and outlines a possible biological mechanism to explain the increased cancers. This suggests that the observed high rates of infant leukemias may be a teratogenic effect from incorporated radionuclides. Doses from environmental emissions from nuclear reactors to embryos and fetuses in pregnant women near nuclear power stations may be larger than suspected. Hematopoietic tissues appear to be considerably more radiosensitive in embryos/fetuses than in newborn babies. Recommendations for advice to local residents and for further research are made.

What makes the learning of physiology in a PBL medical curriculum challenging? Student perceptions

Physiology is an integral component of any medical curriculum. Traditionally, the learning of physiology has relied heavily on systems-based didactic lectures. In 2001, the Nelson R. Mandela School of Medicine (NRMSM; Durban, South Africa) embarked on a problem-based curriculum in which the learning of physiology was integrated with relevant clinical scenarios. Students are expected to gain an understanding of physiology through self-directed research with only certain aspects being covered in large-group resource sessions (LGRSs). It has gradually become evident that this approach has resulted in significant gaps in students' understanding of basic physiological concepts. A survey of student perceptions of needs for physiology was undertaken to gain a better understanding of their perceived problems and also to inform them of proposed curricular changes. Students were asked to what extent they thought physiology was essential for their understanding of pathology, interpretation of patients' clinical signs and presentation of symptoms, and analysis of laboratory results. Students were also invited to detail the difficulties they experienced in understanding in LGRSs on clinical and physiological topics. The results of the survey indicate that greater interaction of students with experts is needed. In particular, students felt that they lacked the basic conceptual foundations essential for the learning and understanding of physiology, since the difficulties that the students identified are mainly terminological and conceptual in nature.

Explorations in statistics: hypothesis tests and P values

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This second installment of Explorations in Statistics delves into test statistics and P values, two concepts fundamental to the test of a scientific null hypothesis. The essence of a test statistic is that it compares what we observe in the experiment to what we expect to see if the null hypothesis is true. The P value associated with the magnitude of that test statistic answers this question: if the null hypothesis is true, what proportion of possible values of the test statistic are at least as extreme as the one I got? Although statisticians continue to stress the limitations of hypothesis tests, there are two realities we must acknowledge: hypothesis tests are ingrained within science, and the simple test of a null hypothesis can be useful. As a result, it behooves us to explore the notions of hypothesis tests, test statistics, and P values.

Are we satisfied with the way we review an article?

Nurses and other healthcare professionals must read professional journals on a regular basis to provide evidence-based care. Because authors often provide little detail on their methodology, it may be difficult to determine whether we should incorporate the conclusions of a study into our clinical practice. In addition, authors and readers may have a limited understanding of statistical analysis. This article gives a brief overview of how to evaluate a manuscript and some basic statistical measures that can be used to evaluate a research study.

Explorations in statistics: standard deviations and standard errors

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This series in Advances in Physiology Education provides an opportunity to do just that: we will investigate basic concepts in statistics using the free software package R. Because this series uses R solely as a vehicle with which to explore basic concepts in statistics, I provide the requisite R commands. In this inaugural paper we explore the essential distinction between standard deviation and standard error: a standard deviation estimates the variability among sample observations whereas a standard error of the mean estimates the variability among theoretical sample means. If we fail to report the standard deviation, then we fail to fully report our data. Because it incorporates information about sample size, the standard error of the mean is a misguided estimate of variability among observations. Instead, the standard error of the mean provides an estimate of the uncertainty of the true value of the population mean.

Microarrays: statistical methods for circadian rhythms

Microarrays are promising tools that are increasingly being applied to the study of circadian rhythms. The large and complex datasets they generate, however, mean they require a new approach on how to design experiments, handle datasets, translate results, and derive conclusions. This technology also requires statistical methods for the correct interpretation of data generated by the microarrays. In this chapter, we provide an overview of analytical methods applied to microarray experiments for the identification of genes with circadian expression.

Regulation of chemokine expression by NaCl occurs independently of cystic fibrosis transmembrane conductance regulator in macrophages

Chronic pulmonary inflammation and infection are the leading causes of morbidity and mortality in cystic fibrosis (CF). While the effect of mutation of the cystic fibrosis transmembrane conductance regulator (CFTR) on airways remains controversial, some groups have demonstrated increases in Na(+) and Cl(-) in CF airway surface liquid compared to normal airways. We investigated the consequences of NaCl on pro-inflammatory chemokine and cytokine production by macrophages. Stimulation of mouse macrophages with increasing amounts of NaCl induced macrophage inflammatory protein-2 (MIP-2) and tumor necrosis factor-alpha (TNF-alpha) production. Further, co-incubation of macrophages with NaCl in the presence of either lipopolysaccharide (LPS) or TNF-alpha synergistically increased MIP-2 production. Both the NaCl and NaCl plus LPS responses were partially dependent on endogenous production and autocrine signaling by TNF-alpha. To investigate the role of CFTR in MIP-2 production, we compared the responses of wild-type and DeltaF508 CF mouse macrophages to NaCl and LPS. The responses of macrophages from both strains were indistinguishable. In addition, CFTR mRNA was not expressed in macrophages. Taken together, these findings suggest that NaCl stimulates MIP-2 production by macrophages through a mechanism that is partially dependent on TNF-alpha but independent of macrophage CFTR expression.

Improving Design and Analysis of Research: Lessons from Clinical Research

There is a clear need to optimise the use of animals in research and to ensure that the studies that are done make a worthwhile contribution to scientific knowledge. Research that has an inappropriate study design, or is improperly analysed or interpreted, may mislead and is not defensible. Published reviews of the quality of design and analysis in clinical studies are used to see what lessons can be drawn in respect of animal research. The need for clear and full reporting of research is discussed.