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.

This chapter discusses two types of descriptive statistics: models of central tendency and models of variability. Models of central tendency describe the location of the middle of the distribution, and models of variability describe the degree that scores are spread out from one another. There are four models of central tendency in this chapter. Listed in ascending order of the complexity of their calculations, these are the mode, median, mean, and trimmed mean. There are also four principal models of variability discussed in this chapter: the range, interquartile range, standard deviation, and variance. For the latter two statistics, students are shown three possible formulas (sample standard deviation and variance, population standard deviation and variance, and population standard deviation and variance estimated from sample data), along with an explanation of when it is appropriate to use each formula. No statistical model of central tendency or variability tells you everything you may need to know about your data. Only by using multiple models in conjunction with each other can you have a thorough understanding of your data.

The behaviors, thoughts, and feelings related to psychopathology are often not of a static nature, but rather change and fluctuate over time in response to changes in daily life situations. Therefore, clinical psychology research can benefit from focusing on how psychopathological features behave over time, as this can provide new perspectives and insights concerning the phenomenology and mechanisms underlying psychopathology. The collection of intensive longitudinal data, consisting of many repeated measurements from single participants, allows for the investigation of several dynamic properties of single or multiple symptoms (and their interrelations). This chapter presents an overview of some major dynamic properties that can be studied with intensive longitudinal data. First, it focuses on several univariate approaches, allowing the examination of one single feature over time. Then it discusses some methods and models to further examine the dynamic relationships between two or more symptoms. For each approach, information is provided on how to calculate simple indices on a more descriptive level, as well as how to model the dynamic features using more complex models.

This chapter provides a basic introduction to descriptive statistics in the context of neuroscience experiments.