2. Curiosity and Skepticism: A Healthy Mindset

This opening chapter is focused on helping you develop a healthy mindset for scientific investigation. This mindset is composed of two opposing modes of operation: curiosity and skepticism.

Learning Objectives In this chapter, you will learn

  • the dual “modes” of curiosity and skepticism 1

Running Example: Detective Enola

Examples are helpful! This book is full of examples of curiosity and skepticism in-action. But before we introduce any of the detailed scientific case studies, let’s use the context of a detective story for this chapter: The owner of a large fortune has just been found in his manor with a knife in his back, and our detective Enola suspects one of his kin is responsible. As we’ll see below, the protagonist of a murder-mystery exemplifies the dual mindset of curiosity and skepticism.

2.1. Not a Procedure, but Rather a Mindset

Upfront, it is important to note that curiosity and skepticism are not a fixed procedure, but rather two modes of a mindset. The items below are not so much a checklist to go through sequentially, but rather a set of “moves” we can employ during the scientific investigative process.

An analogy: The U.S. legal system is based on the idea that truth can be found by pitting two rational opposing forces against each other. Through defense and prosecution teams working against each other, presenting evidence and finding holes in each others’ arguments, society can come closer to the truth and hence justice.

The back-and-forth between curiosity and skepticism is similar: two modes we should switch between during scientific investigations. By engaging in curious behavior, we can ask questions and pose answers. By engaging in skepticism, we can identify flaws and improve our analysis. Let’s look at the two modes in greater detail.

2.2. Curiosity

The mode of curiosity is characterized by openness: to exploration, to new questions, to approaches, and to possible answers. Some of the “moves” in the curious mode include:

2.2.1. Asking questions

The very notion of curiosity is closely associated with asking questions; we describe a child as “curious” if they tend to ask a lot of questions. Most scientific investigations start with some sort of “global question” that kicks off a research effort. However, the process of asking questions doesn’t stop there.

As Tukey [Tuk77] writes in the context of exploratory data analysis, TODO…

Running Example: Detective Enola

In a murder-mystery, a detective will generally have one overarching question: “whodunnit?” But to answer this driving question, Enola will have to pose and answer a variety of sub-questions: Who are the suspects? What facts do I know? Why is the eldest son being cagey? These questions can help the detective decide what data to gather next.

2.2.2. Gathering data

Questions are useful in part because they focus our efforts. One of the main ways we make progress in answering our questions is to gather data.

In scientific modeling, we can gather both experimental data (from a physical experiment) and synthetic data (from a simulation). Experimental data requires running a physical experiment, necessitating time and hardware but serving as the ground-truth for reality. Lightweight computational models can be run far more quickly to suggest possible trends, which can aid in planning physical experiments.

Note that we can gather data in a curious exploratory way, or in a more skeptical testing fashion. The data collection we plan in the curious mode will tend to be more ad hoc. Perhaps we plan out a quick pilot study, or get our hands on some archival data that is only somewhat associated with our question. The point of gathering data in the curious mode is not to be comprehensively formal; rather, it is to move quickly and refine our questions so we can move towards the greater clarity of a formal hypothsis.

Running Example: Detective Enola

Even before our detective formulates an answer to a question, she will try a variety of “moves” to advance her investigation. Enola might try dusting for prints at the scene of the crime, interview eyewitnesses to search for plausible motives, or send the murder weapon out to the lab for a forensic test. For Enola, the coroner’s examination of the body provides a time of death, blunt trauma on the face, and a knife-wound as the cause of death, all of which narrow the set of possible hypotheses.

2.2.3. Posing hypotheses

The primary output of the curious mode is a testable hypothesis. This is a synthesis of the available data and initial questions, one which ideally addresses our original global question. Arriving at a hypothesis is often the departure point from the curious mode to the skeptical one.

Posing a hypothesis requires interpreting the data. This is where domain-specific knowledge of our problem is essential: Only a specialist in your field can interpret the data in terms of your discipline’s accepted conventions. However, regardless of one’s discipline, there is one criterion that all hypotheses must meet—a scientific hypothesis must be testable.

As Popper [Pop05] wrote on the philosophy of science, a scientific hypotheses should be specific enough to be falsifiable; that is, able to be shown to be false. Falsifiability is a reasonable criteria for determining what is a testable hypothesis. If our hypotheses is too flexible or too vague to be testable, this is a sign that the hypothesis needs refinement! Usually, a falsifiable hypothesis makes predictions about what would happen under a certain experimental setting; in this case, the hypothesis would be falsified if the prediction failed to hold.

Running Example: Detective Enola

Enola wants to know “whodunnit,” so her hypothesis should include a suspect, motive, and opportunity. Enola suspects the eldest son is responsible, as the household staff intimated that he is greedy and impatient to inherit. She makes sure to incorporate all the data she has gathered in formulating her hypothesis; she thinks the son stabbed the victim in the back with a knife around the time of death, causing the victim to fall forward onto their face.

2.3. Skepticism

The mode of skepticism is characterized by rejection: rejecting hypotheses, finding flaws in the data and methods, and even probing the importance of questions. Some of the “moves” in the skeptical mode include:

2.3.1. Testing hypotheses

Once we have posed a hypothesis, it is important to subject it to testing. As noted under Posing hypotheses above, any scientific hypotheses must be testable, usually by being falsifiable. If the hypothesis is untestable, you need to go back and refine your hypothesis! However, you might not recognize your hypothesis as untestable until you switch over to the skeptical mode.

Remember, a skeptic would not use a “gentle” test. Try to formulate a strenuous test of your hypothesis. What would a worst-case look like for your hypothesis? What would be a convincing argument, even to your toughest critic?

We’ll see more about this in Chapter TODO, but as a preview, you should use data that are independent of your exploratory data to test a hypothesis. In the parlance of machine learning, testing a model on the same data used to train is a biased, foolish way to assess a model [JWHT13]. If your hypothesis is true, then your findings should hold for an independently-gathered dataset.

Running Example: Detective Enola

Enola is confident in her hypothesis, so she makes an accusation of the son. However, he denies the accusation and produces a compelling alibi: He was with the Butler all through the time of death, and the Butler is beyond reproach. Clearly something about Enola’s hypothesis does not hold up.

If your hypothesis does not hold, then you need to trace back your reasoning. This includes both your data and your interpretation of the facts.

2.3.2. Questioning the data

Sometimes, the data themselves are in error. Experiments can have confounding variables, simulations can be misconfigured or unconverged, and even simple matters of updated inputs without re-evaluated outputs can cause great confusion 2. When your hypothesis does not hold, it is important to hunt down and eliminate these kinds of issues.

As we will see in Chapter TODO, variability in measurements can be either real or induced. Real variability affects the quantity we aim to study, while induced variability affects our measurement only. Determining whether the variability we see is due to real or induced sources helps us narrow our search to either before or after the phenomenon we seek to study. If there is an explanation for induced variability, then our measurement is subject to a mistake. Collecting new data while eliminating such sources of induced variability will help us more accurately understanding our object of scientific study.

Running Example: Detective Enola

Enola thinks there is something fishy about the time of death, and has a longer conversation with the coroner. A closer inspection of the body confirms the time of death, so the data do not seem to be in error….

2.3.3. Questioning the interpretation

Sometimes the data are not in error, but rather our interpretation is incorrect. This can mean we made an error in our reasoning, or that we were missing some key facts about the scenario at hand.

Real variability with an assignable cause is called an anomaly: If we see unexpected variability in our measurements that seems to arise from some consistent phenomena, we might find that a previously unknown phenomenon can explain this behavior 3. Such anomalous variability can lead to a scientific discovery. However, a lack of variability can also lead to scientific advancement.

Before Einstein formulated his theory of relativity (TODO circa 19XX), all waves had been observed to travel within some medium. Hence, scientists at the time believed that light, exhibiting wave-like properties, must travel within a medium called the lumineferous aether. Scientists like Albert Michelson (TODO citation needed) carried out experiments to detect the “aether wind” due to the motion of Earth through this aether, but failed to detect any change in the speed of light. Einstein’s contribution overturned this understanding of physics, and our modern understanding is that light travels through a vacuum at a speed constant to all observers.

Running Example: Detective Enola

In conversation with the coroner, Enola realizes that the knife wound and face trauma could have occurred at very different times. If the victim were killed at one time and their body clumsily moved later, this could account for both wounds. Upon further investigation, Enola finds a cunning mechanism hidden in the victim’s chair: The father died by a deadly trap while the son was with the Butler, and the son returned much later to rearrange the scene to make it appear to be a much simpler murder. By employing both curiosity and skepticism, Enola solved the case.

2.4. Uncertainty and Mindset

This book is focused on modeling in the presence of uncertainty. TODO

(Modeling is iterative; moving between the modes of curiosity and skepticism to reduce uncertainty to a level sufficient to answer your modeling question)

2.5. Learning the Mindset

These ideas about curiosity and skepticism are important, but if they remain ideas only they are useless. It is important for you to go beyond simply knowing about these ideas and start using them in your own scientific practice. However, this is easier said than done!

To help you make use of the curious and skeptical modes, we have a few suggestions:

2.5.1. Using this book

The case studies in this book are written to emphasize the modes of curiosity and skepticism. Keep an eye out for the following boxes, as they will highlight elements of the case studies that correspond to curiosity- or skepticism-in-action.


Here’s an example of curiosity-in-action.


Here’s an example of skepticism-in-action.

With a bit of imagination, you can generalize these sorts of insights to your own context. While reading this book, think about ways the same kinds of observations or issues could show up in your own scientific investigations. Better yet, try practicing some of the techniques suggested in this book on your own problems!


I am grateful to Bill Behrman, who first introduced me to the idea of curiosity and skepticism as modes of operation.


We will see an example of mismatched inputs and outputs in Chapter TODO.


We will see an example of lurking variables and techniques to detect them in Chapter TODO.