Biodiversity monitoring programmes should be designed with sufficient statistical power to detect population change. Here we evaluated the statistical power of monitoring to detect declines in the occupancy of forest birds on Christmas Island, Australia. We fitted zero-inflated binomial models to 3 years of repeat detection data (2011, 2013 and 2015) to estimate single-visit detection probabilities for four species of concern: the Christmas Island imperial pigeon Ducula whartoni, Christmas Island white-eye Zosterops natalis, Christmas Island thrush Turdus poliocephalus erythropleurus and Christmas Island emerald dove Chalcophaps indica natalis. We combined detection probabilities with maps of occupancy to simulate data collected over the next 10 years for alternative monitoring designs and for different declines in occupancy (10–50%). Specifically, we explored how the number of sites (60, 128, 300, 500), the interval between surveys (1–5 years), the number of repeat visits (2–4 visits) and the location of sites influenced power. Power was high (> 80%) for the imperial pigeon, white-eye and thrush for most scenarios, except for when only 60 sites were surveyed or a 10% decline in occupancy was simulated over 10 years. For the emerald dove, which is the rarest of the four species and has a patchy distribution, power was low in almost all scenarios tested. Prioritizing monitoring towards core habitat for this species only slightly improved power to detect declines. Our study demonstrates how data collected during the early stages of monitoring can be analysed in simulation tools to fine-tune future survey design decisions.

The current study explored the impact of genetic relatedness differences (ΔH) and sample size on the performance of nonclassical ACE models, with a focus on same-sex and opposite-sex twin groups. The ACE model is a statistical model that posits that additive genetic factors (A), common environmental factors (C), and specific (or nonshared) environmental factors plus measurement error (E) account for individual differences in a phenotype. By extending Visscher’s (2004) least squares paradigm and conducting simulations, we illustrated how genetic relatedness of same-sex twins (HSS) influences the statistical power of additive genetic estimates (A), AIC-based model performance, and the frequency of negative estimates. We found that larger HSS and increased sample sizes were positively associated with increased power to detect additive genetic components and improved model performance, and reduction of negative estimates. We also found that the common solution of fixing the common environment correlation for sex-limited effects to .95 caused slightly worse model performance under most circumstances. Further, negative estimates were shown to be possible and were not always indicative of a failed model, but rather, they sometimes pointed to low power or model misspecification. Researchers using kin pairs with ΔH less than .5 should carefully consider performance implications and conduct comprehensive power analyses. Our findings provide valuable insights and practical guidelines for those working with nontwin kin pairs or situations where zygosity is unavailable, as well as areas for future research.

Despite the particular relevance of statistical power to animal welfare studies, we noticed an apparent lack of sufficient information reported in papers published in Animal Welfare to facilitate post hoc calculation of statistical power for use in meta-analyses. We therefore conducted a survey of all papers published in Animal Welfare in 2009 to assess compliance with relevant instructions to authors, the level of statistical detail reported and the interpretation of results regarded as statistically non-significant. In general, we found good levels of compliance with the instructions to authors except in relation to the level of detail reported for the results of each test. Although not requested in the instructions to authors, exact P-values were reported in just over half of the tests but effect size was not explicitly reported for any test, there was no reporting of a priori statistical analyses to determine sample size and there was no formal assessment of non-significant results in relation to type II errors. As a first stage to addressing this we recommend more reporting of a priori power analyses, more comprehensive reporting of the results of statistical analysis and the explicit consideration of possible statistical power issues when interpreting P-values. We also advocate the calculation of effect sizes and their confidence intervals and a greater emphasis on the interpretation of the biological significance of results rather than just their statistical significance. This will enhance the efforts that are currently being made to comply with the 3Rs, particularly the principle of reduction.

Dr Nick Martin has made enormous contributions to the field of behavior genetics over the past 50 years. Of his many seminal papers that have had a profound impact, we focus on his early work on the power of twin studies. He was among the first to recognize the importance of sample size calculation before conducting a study to ensure sufficient power to detect the effects of interest. The elegant approach he developed, based on the noncentral chi-squared distribution, has been adopted by subsequent researchers for other genetic study designs, and today remains a standard tool for power calculations in structural equation modeling and other areas of statistical analysis. The present brief article discusses the main aspects of his seminal paper, and how it led to subsequent developments, by him and others, as the field of behavior genetics evolved into the present era.

This article provides an accessible tutorial with concrete guidance for how to start improving research methods and practices in your lab. Following recent calls to improve research methods and practices within and beyond the borders of psychological science, resources have proliferated across book chapters, journal articles, and online media. Many researchers are interested in learning more about cutting-edge methods and practices but are unsure where to begin. In this tutorial, we describe specific tools that help researchers calibrate their confidence in a given set of findings. In Part I, we describe strategies for assessing the likely statistical power of a study, including when and how to conduct different types of power calculations, how to estimate effect sizes, and how to think about power for detecting interactions. In Part II, we provide strategies for assessing the likely type I error rate of a study, including distinguishing clearly between data-independent (“confirmatory”) and data-dependent (“exploratory”) analyses and thinking carefully about different forms and functions of preregistration.

Previous European guidance for environmental risk assessment of genetically modified plants emphasized the concepts of statistical power but provided no explicit requirements for the provision of statistical power analyses. Similarly, whilst the need for good experimental designs was stressed, no minimum guidelines were set for replication or sample sizes. Furthermore, although substantial equivalence was stressed as central to risk assessment, no means of quantification of this concept was given. This paper suggests several ways in which existing guidance might be revised to address these problems. One approach explored is the `bioequivalence' test, which has the advantage that the error of most concern to the consumer may be set relatively easily. Also, since the burden of proof is placed on the experimenter, the test promotes high-quality, well-replicated experiments with sufficient statistical power. Other recommendations cover the specification of effect sizes, the choice of appropriate comparators, the use of positive controls, meta-analyses, multivariate analysis and diversity indices. Specific guidance is suggested for experimental designs of field trials and their statistical analyses. A checklist for experimental design is proposed to accompany all environmental risk assessments.

The aim of the present study was to quantify the inter-day reliability of force platform data from horses at the trot. The subjects were ten horses judged to be sound on the basis of clinical evaluation and similarity of ground reaction force peaks of left and right limbs. The same handler trotted horses in hand for four consecutive days over a force platform at their natural and comfortable velocity between 2.70 and 3.60 m s−1. For each horse, velocity was controlled in a range of±0.15 m s−1. Five contacts per limb were recorded on each day. Custom-written code in MATLAB was used to smooth the data and time-normalize to 101 data points. The agreement boundary was used to determine the reliability of seven force variables. Stance duration, peak vertical force and vertical impulse were the most reliable variables (<10% variability in the mean), while peak braking and propulsive forces and impulses had larger variation across the three days (>20% variability in the mean). Variables with low variability may require fewer animals, trials and days to obtain accurate data while maintaining adequate statistical power. These results may act both as a reference when studying variability in lame horses and as a guide for planning future equine kinetic studies.

Objective: To compare two methods of measuring willingness to pay (WTP): closed-ended questions with and without follow-up.

Methods: A measurement experiment based on dichotomous choice contingent valuation survey data is reported. Marginal WTP estimates for alleviation of rheumatoid arthritis symptoms resulting from treatment with a novel anti-rheumatic agent, cA2 (TNF-α blockade), were calculated. Monte Carlo simulations were undertaken to evaluate the methods with respect to their statistical power.

Results: The estimated marginal WTP values using closed-ended questions with and without follow-up were DKK 637 (US $91) and DKK 1,268 (US $181), respectively. A Wilcoxon's signed-rank test showed that the difference of DKK 631 was significant. Moreover, including a follow-up question increases the precision of the result. Monte Carlo simulations showed that trade-offs between power (i.e., the probability of a correct rejection of a false null hypothesis), efficiency, and size may exist in the two models.

Conclusions: There was a significant difference between the WTP estimates when using closed-ended questions with and without follow-up. When choosing between the models, however, power, efficiency, and size could be used as selection criteria.

It has been postulated that the variation in an animal's body temperature during different phases of bone deposition can be estimated from the variation in the ratio of two oxygen isotopes in phosphate recovered from its fossil. Using data from a fossil Tyrannosaurus rex skeleton, the previous researchers failed to reject the null hypothesis that the variation found in bones from the body core is significantly less than that found in the extremities; they also failed to reject the null hypothesis that the coldest and hottest parts of the body differed by less than 4 °C. This suggests that either both null hypotheses are true, or that they are false but the statistical tests used were not powerful enough to detect this. Here I use calculation of confidence intervals to differentiate between these two alternatives. In the first case, I find that the data are consistent with the variance in both the core and the extremity being up to 2.5 times that of the other. In the second case, I show that observations are consistent with temperature differences ranging from 2.6 °C to 6.4 °C. Hence, neither case provides any real support for the hypothesis that bone deposition in this specimen occurred under essentially thermostable conditions.

The power for detection of quantitative trait loci (QTL) using marker information was compared in several schemes differing in the mating type and the number of parents involved. An experiment based on an F2 population of fixed size obtained by crossing two lines differing phenotypically for a single trait was simulated, assuming that QTLs could be fixed or segregating in the lines crossed. Different additive and dominant QTL effect values and allele frequencies were considered covering a range of different favourable situations for the detection of the QTL. Comparison was done by regression using flanking marker information. Mating animals at the F1 generation level minimizing relationships was not worse than mating at random or maximizing relationships. The number of parents used affected the power of the experiment when the QTL was segregating in the original crossed lines. Differences in power were mainly related to the number of males from the original line. When the power of the experiment was high as a result of genetic hypothesis assumed, considering several males increased the power. However, when the genetic hypothesis assumed led there to be less power to detect a QTL, the power was higher when fewer males were used.