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The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion. Here we argue that statistics, as an interdisciplinary scientific field, plays a substantial role both for the theoretical and practical understanding of AI and for its future development. Statistics might even be considered a core element of AI. With its specialist knowledge of data evaluation, starting with the precise formulation of the research question and passing through a study design stage on to analysis and interpretation of the results, statistics is a natural partner for other disciplines in teaching, research and practice. This paper aims at highlighting the relevance of statistical methodology in the context of AI development. In particular, we discuss contributions of statistics to the field of artificial intelligence concerning methodological development, planning and design of studies, assessment of data quality and data collection, differentiation of causality and associations and assessment of uncertainty in results. Moreover, the paper also discusses the equally necessary and meaningful extensions of curricula in schools and universities to integrate statistical aspects into AI teaching.

The Posner cueing paradigm is one of the most widely used paradigms in attention research. Importantly, when employing it, it is critical to understand which type of orienting a cue triggers. It has been suggested that large effects elicited by predictive arrow cues reflect an interaction of involuntary and voluntary orienting. This conclusion is based on comparisons of cueing effects of predictive arrows, nonpredictive arrows (involuntary orienting), and predictive numbers (voluntary orienting). Experiment 1 investigated whether this conclusion is restricted to comparisons with number cues and showed similar results to those of previous studies, but now for comparisons to predictive colour cues, indicating that the earlier conclusion can be generalized. Experiment 2 assessed whether the size of a cueing effect is related to the ease of deriving direction information from a cue, based on the rationale that effects for arrows may be larger, because it may be easier to process direction information given by symbols such as arrows than that given by other cues. Indeed, direction information is derived faster and more accurately from arrows than from colour and number cues in a direction judgement task, and cueing effects are larger for arrows than for the other cues. Importantly though, performance in the two tasks is not correlated. Hence, the large cueing effects of arrows are not a result of the ease of information processing, but of the types of orienting that the arrows elicit.

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.

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Exploratory spatial data analysis has a series of aims including determining spatial structure in the data, describing and visualizing geographical distributions, exploring spatial dependencies, measuring heterogeneity and identifying outliers. To quantify these phenomena a rich variety of statistics has been proposed. Standard methods use all the data for the entire area under study, yet this area has usually been arbitrarily bounded and may include quite distinctive geographic features. Local statistics are relatively independent of the global boundaries and they attempt to quantify how close a given datum is to the values in its neighbourhood. Since each local statistic focuses on slightly different aspects of the data the use of more than one is suggested. Interactive graphics methods help to link the information from different local statistics and dynamic tools can be used to visualize the effects of changing the neighbourhood de®nition.