This chapter introduces the basic concepts of electroencephalography (EEG) and creates a foundation for further concepts. EEGs are graphical representations of continuous synaptic activity occurring in the pyramidal neurons within the superficial cortical surfaces. The EEG shows an arrangement of channels, each consisting of two electrodes, that record electrical potentials from the underlying cortex and display it in the form of waveforms. The appearance of each waveform is governed by three simple rules of polarity. Electrodes are small circular metallic discs that can be affixed to the scalp with glue or collodion and connected to the EEG machine. They are placed using the standardized international 10-20 system. Pairs of electrodes (channels) are displayed in different arrangements called montages which can be used to localize a waveform on the cortical surface. Display parameters such as sensitivity and filter settings may also modify the appearance of the waveforms. Waveforms may be described based on their frequency and rhythm. Features of the normal adult EEG as well as strengths and limitations of electroencephalography are also discussed in this chapter.

Perception of statistical relations often deviates from mathematically correct values: individuals infer a statistical relation when there is none, find a relationship that has the opposite sign of the genuine contingency, or ignore relevant third variables in the observed sample. Contingency assessment is based on sampling and integration of information, whether sampled from memory or from the environment. Information sampling in turn may depend on various factors, including the valence of events observed, direct consequences of sampling a piece of information, the amount of information given per sample, or prior knowledge and beliefs. Whenever individuals sample information, such factors come into play and may lead to asymmetries in the available information. For instance, more positive events than negative events might be observed, more instances of one alternative as compared with another, more information about the ingroup than the outgroup, and so on. Such asymmetries or skewed frequencies might provide the basis for pseudocontingency inferences. According to the pseudocontingency heuristic, contingencies are inferred on the basis of observed marginal frequencies: When observing skewed marginal frequencies of two variables (e.g., option A more frequently than option B and gains more often than losses), individuals relate the frequent observations per variable with each other (e.g., option A and gains) as well as the infrequent observations (e.g., option B and losses). In this chapter, we review empirical evidence on the influence of information sampling on the inference of genuine and pseudocontingencies. We provide some answers to the following questions: Which information is sampled? When do reasoners use marginal frequencies in order to form a judgment and thereby infer a pseudocontingency? May information sampling foster genuine contingency assessment? And what part do expectations or prior beliefs play?

What are statistics and why do we need them? This chapter introduces descriptive statistics and then creates a bridge from describing data concisely to answering questions using hypothesis testing and inferential statistics. The chapter leads the reader to an understanding of how descriptive statistics summarize and communicate meaning, based on data, and how they underpin inferential statistics. Research study examples, figures, and tables throughout the chapter explain the topics addressed by applying the ideas discussed. The chapter begins with the basics of descriptive statistics – normal distributions, options for displaying frequencies, measures of central tendency and variability, and correlations. The transition to inferential statistics covers standardization and the z-score, sampling, confidence intervals, and basics of hypothesis testing including Type I and II errors. We then introduce inferential statistics using three methods – t-tests, one-way analysis of variance (ANOVA), and chi-square tests.

The chapter ’#StatsWithCats’ shows some statistical methods to interpret and visualise the cat-related online data. The selected sociolinguistic variables are the social media platforms and the cat account types. The chapter takes frequencies and crosstabs to describe linguistic variation across four social media platforms and four cat account types. The selected linguistic variables refer to the choices of non-meowlogisms and meowlogisms on Facebook, Instagram, Twitter, and Youtube as well as in collective, for-profit celebrity, working-for-cause, and individual cat accounts. Additionally, the chapter uses social network analysis to illustrate the networks in cat-related digital spaces.