top of page

3: What does good science look like?

This chapter discusses terminology around facts, opinions, data, and information. Each of these terms is defined and the chapter explains how they are integrated together to form knowledge that can improve public policy within the policing context. The chapter then explains that good science should involve a plausible mechanism, temporal causality, statistical association, and the rejection of competing explanations. After discussing good science, the chapter outlines ways that pseudoscience and science denialism can infiltrate public debate.

Glossary terms in this chapter

Fact: A fact is a statement or detail that can be proven to be true or to exist.
Data: Data are collections of facts and statistics compiled for reference or to aid analysis.
Opinion: An opinion is a feeling, viewpoint or a person's perception of a given item or event.
Informed opinion: An informed opinion is an opinion grounded in knowledge of the available facts and carefully considered Scientific principles. An informed opinion relies more on scientific evidence than just limited personal experience.
Information: Information is infused with topic relevance and related meaning and context. Information can, however, be unstructured.
Context: Context is the wider environment in which data and information reside, often represented by a knowledge of the broader subject area.
Knowledge: Knowledge is data and information within a given context that has meaning, and a particular interpretation, and reflects a deep theoretical and practical understanding of a subject.
Scientific evidence: Scientific evidence is the accumulated wisdom from systematic studies and observations that can help a policy maker reach a conclusion about a policy choice. Scientific evidence (at least in the context of this book) is proof that can support a position or claim of effectiveness.

Predictive policing: In the context of place, it is the use of historical data to create a spatiotemporal forecast of areas of criminality or crime hot spots that will be the basis for police resource allocation decisions with the expectation that having officers at the proposed place and time will deter or detect criminal activity.
Plausible mechanism: A plausible mechanism is a reasonable or persuasive process that links a cause to an effect.
Temporal causality: Temporal causality means that any effect should occur during, or after, a related cause.
Statistical association: Statistical association helps us confirm a demonstrated relationship between a variable and the result of changing that variable.
Rejecting competing explanations: Rejecting competing explanations for an observed outcome means making a concerted and good faith effort to rule out other possible reasons why a study result occurred.
Internal validity: Refers to the legitimacy of inferences we make about the causal relationship between two things. Strong internal validity in a causal relationship means changes in one thing effect or cause a change in the other.
Pseudoscience: Pseudoscience is a collection of practices and beliefs that are mistakenly thought to be grounded in the scientific method.
Science denialism: Science denialism is the use of rhetorical arguments to give the appearance of legitimate debate with the ultimate aim of rejecting a proposition on which a scientific consensus exists.

Additional information and links

Opinions and informed opinions

Much of the chapter takes you through the difference between opinion, informed opinion, knowledge and context. One page that simply describes the difference between these concepts (though perhaps using the terms slightly differently) is on the distinction between data and evidence. A researcher at the university of Kent has a deeper dive if you feel like exploring more advanced discussions. 

Alex Edmans has an interesting and fun TEDx talk about what to trust in our information saturated world below. 

Bad science (pseudoscience and denialism)

On bad science and science denialism one of the best people who articulates the problem is Dr. Ben Goldacre. while his area is medical research, the lessons are very translatable. His TED talk is particularly illuminating. Doctor and epidemiologist Ben Goldacre shows us, at high speed, the ways evidence can be distorted, from the blindingly obvious nutrition claims to the very subtle tricks of the pharmaceutical industry.




Another expert in this area is Michael Shermer. In his TED talk, he debunks myths, superstitions and urban legends -- and explains why we believe them. In this smart and very amusing talk, Shermer outlines how easy it is to be fooled. 

Reducing Crime podcast episode

There really isn't a specific Reducing Crime podcast episode that directly links to the topic of this chapter; however, I'm suggesting this episode with Keven Bethel - former Deputy Commissioner for the Philadelphia Police Department - because he discusses his transition to really appreciating the value of data (by implication, over gut feeling) as a vital tool for police decision-making. 

Kevin Bethel (#24)

Kevin Bethel is a retired Philadelphia Deputy Police Commissioner and now Chief of School Safety for the Philadelphia School District. We chat about the school-to-prison pipeline and his work rethinking the role of police in schools. His diversion program has reduced school arrests by 71 percent.

bottom of page