Scientific inquiry/ Critical thinking I

Full course description

The scientific inquiry/critical thinking learning arc in the bachelor contains teaching and learning activities that help you develop scientific reasoning and problem solving competencies, effective research methods and statistical analysis skills, and computational literacy. This first module prioritizes critical thinking and statistics.

Critical thinking involves more than just a critical attitude: it is a collection of complex cognitive skills which are at the core of human thinking and reasoning. In the first half of this module, you are introduced to some of the most important obstacles to understanding ourselves and the world around us. We appear to be cursed by biases, fallacies and illusions. You will learn to use some of the basic tools of scientific inquiry such as logic, basic statistical reasoning and information literacy. These tools enable you to deal with uncertainty and help you to think straight about psychology. By using these tools, you will dissect arguments and analyse their core structure.

The theoretical introductions of the main themes in academic psychology in the first two core modules are complemented with a more practical introduction of these complex cognitive skills that are important for scientific inquiry and critical thinking. We aim to build bridges of meaning between, for example, research on human reasoning and the basics of logic. During the course, you will also practice your critical thinking skills in a more informal manner with debates.

Empirical researchers test theories based on observed data. It is therefore important to acquire a set of skills that allow you to get to know these observed data. Hence, in the second half of this module you also learn how to apply various descriptive statistical techniques that help describe/summarize the univariate distribution of a single categorical or quantitative variable (including histogram, mode, mean, median, standard deviation, interquartile range) and the bivariate distribution of two categorical or quantitative variables (including correlation, association, linear regression, contingency tables).

Finally, emphasis will be placed on the logic behind the statistical reasoning process when you study concepts that are central in inferential statistics (incl. random experiment, sample space, events, (un-) conditional probability, statistical (in)dependence, random variables, probability distribution, expected value and standard deviation, density curve, simple random sampling, parameters and (unbiased) estimators, population distribution, distribution of sample scores, sampling distribution, standard error, central limit theorem, null- and alternative hypothesis, one vs. two-tailed test, test statistic, z-test, p-value, significance level, power, Type I- and Type II-errors, confidence interval). These topics form the theoretical background that is necessary to understand the statistical techniques that are covered in the remainder of the bachelor program.

There are skills sessions to help prepare you to independently run statistical analyses (which is a learning goal in subsequent SICT modules of the bachelor). These sessions also aim to familiarise you with elementary programming concepts as you learn to use commands for manipulating and analyzing data in scripts.

There is no assessment for this module. You will only receive feedback on completed assignments.