Species identification skills predict in-depth knowledge about species

To raise biodiversity awareness effectively, communicators should be aware of knowledge levels in their audiences. Species identification skills have been used in the past as a measure of what people know about species, yet it is not known whether they serve as good indicators. To study the link between species identification and in-depth species knowledge, we presented an animal knowledge test to an online audience of over 7,000 Dutch adults, and used correlation and regression analyses to determine the extent to which species identification predicts in-depth knowledge about species’ origin, habitat, diet, and behavior. We found that in-depth knowledge was higher in those who correctly identified species as compared with those who did not correctly identify species, for all four types of in-depth knowledge. Moreover, as compared to alternative variables (work, age, gender, and educational level), species identification was by far the best predictor for in-depth knowledge about species. However, species identification levels were generally higher than levels of in-depth knowledge, and knowledge gaps and misconceptions were uncovered. The results confirm the value of species identification tests, but also highlight limitations and challenges that should be taken into account when establishing knowledge levels and communicating biodiversity

Seven steps toward more transparency in statistical practice

Item does not contain fulltextWe argue that statistical practice in the social and behavioural sciences benefits from transparency, a fair acknowledgement of uncertainty and openness to alternative interpretations. Here, to promote such a practice, we recommend seven concrete statistical procedures: (1) visualizing data; (2) quantifying inferential uncertainty; (3) assessing data preprocessing choices; (4) reporting multiple models; (5) involving multiple analysts; (6) interpreting results modestly; and (7) sharing data and code. We discuss their benefits and limitations, and provide guidelines for adoption. Each of the seven procedures finds inspiration in Merton’s ethos of science as reflected in the norms of communalism, universalism, disinterestedness and organized scepticism. We believe that these ethical considerations - as well as their statistical consequences - establish common ground among data analysts, despite continuing disagreements about the foundations of statistical inference.8 p

Crowdsourcing hypothesis tests: Making transparent how design choices shape research results

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer fiveoriginal research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams renderedstatistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.</div

Distributional inference: the limits of reason

Summary Distributional inference: the limits of reason Science advances by combining rational arguments and empirical information. In fields like philosophy and pure mathematics, emphasis is laid on the rational arguments, whilst in the applied sciences the collection and interpretation of data are the field of interest. In mathematical statistics one tries to combine these aspects. The primary goal is to make statistical inferences about something unknown. Such inferences can be of help in further discussion, e.g. in selecting a decision. The methods should not depend on ‘the intentions that might be furthered by utilizing the knowledge inferred’1. When the available data are too limited, then different procedures may yield different inferences. The statistician should refrain from providing a specific inference in case the differences are ‘too large’. When such an inference can be given, this inference should be accompanied by a statement about the uncertainty of the inference. This could be done by providing a distributional inference, or by providing the results of different approaches. An example is as follows. Ornithologist G.Th. de Roos is observing a population of Ruddy Turnstones (Arenaria Interpres) on the Frisian island Vlieland. Some of these birds are ringed, however the ring-number is not always observable, e.g. because another bird is blocking the view. After how many days of observing is it safe to assume that all ringed birds in the population have been observed at least once? This question can be answered by constructing a distributional inference about the number of present, yet unseen, ringed birds, including a probability statement about the hypothesis that all ringed birds have been seen. Of course, the results depend in some way on the probabilistic assumptions one makes, and on the statistical principles one follows. The first part of this thesis consists of ‘finger exercises’ illustrating that information about the unknown can only be of value if the mechanism generating the information is (sufficiently well) known. In probability theory, information is incorporated by conditioning to it. This generates difficulties in statistical practice, because unknown aspects are involved in the joint distribution of the random variables X and Y that are behind the observations x and the unknown y. Firstly, this is extensively exemplified by a die-rolling game. From the information ‘the number of eyes is even’ one cannot conclude automatically: ‘the probability that a six has been thrown, equals one third’. The way in which the source of information operates, should be incorporated in the statistical model. Secondly, a similar example, the two-envelopes problem, is considered. Again, the difficulties involving the numerical specification of conditional probabilities are in the forefront. The second and most important part deals with the situation where one has a random sample x1, . . . , xn from a distribution with density f. The goal is to use the sample to form an estimate of f or, almost equivalently, to generate a distributional inference about y(= xn+1). A new method is discussed to estimate the density f, where ‘initial knowledge’ of f is incorporated in the model. This is done by specifying a probability density ψ as the ‘initial guess’ for f. Also the degree of confidence in this ψ is quantified and incorporated in the method. By means of a multi-modal approach, incorporating aspects from both Classical and Bayesian statistics, and on basis of the sample x, ‘initial guess’ ψ (and the degree of confidence in ψ), an estimate ˆ f of f is generated. When the initial guess ψ is not unreasonable, this density estimate performs better, in general, than the generally used kernel methods. This is no surprise, since the kernel method makes no use of ψ. It is at this point unclear how the comparison will turn out when ψ is incorporated in the kernel method. To study the applicability of the developed method, an extensive data set about the pollution of Dutch waters is considered. Previous investigations showed that the different concentrations of pollutants can reasonably well be described by lognormal distributions. A complication is that the concentrations can only be measured when they are above a certain detection threshold. The density estimation theory of this thesis, adapted to mentioned complication, is used to ‘fine-tune’ the ‘initial guess’ of lognormality to the data. The resulting density estimates are better than the density estimates obtained previously by fitting lognormal densities. The density estimation theory of this thesis can usefully be applied to the goodness of fit context where a statement is required about the truth or falsity of the hypothesis H0: f = ψ. The resulting goodness of fit tests have interesting relations with the well-known χ2-test, Kolmogorovs test, and Neymans ‘smooth tests’. To emphasize the usefulness of distributional inference, an example from the interface of multivariate analysis and time-series analysis is discussed.

Crowdsourcing hypothesis tests: making transparent how design choices shape research results

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams rendered statistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim