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Hypothesis Statement For Intelligence

Aims and Hypotheses

Saul McLeod published 2014


An aim identifies the purpose of the investigation. It is a straightforward expression of what the researcher is trying to find out from conducting an investigation.

The aim typically involves the word “investigate” or “investigation”.

For example:

  • Milgram (1963) investigated how far people would go in obeying an instruction to harm another person.

  • Bowlby (1944) investigated the long-term effects of maternal deprivation.

Types of Hypotheses

A hypothesis (plural hypotheses) is a precise, testable statement of what the researchers predict will be the outcome of the study.

This usually involves proposing a possible relationship between two variables: the independent variable (what the researcher changes) and the dependant variable (what the research measures).

In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Briefly, the hypotheses can be expressed in the following ways:

  • The null hypothesis states that there is no relationship between the two variables being studied (one variable does not affect the other). It states results are due to chance and are not significant in terms of supporting the idea being investigated.

  • The alternative hypothesis states that there is a relationship between the two variables being studied (one variable has an effect on the other). It states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

In order to write the experimental and null hypotheses for an investigation, you need to identify the key variables in the study. A variable is anything that can change or be changed, i.e. anything which can vary. Examples of variables are intelligence, gender, memory, ability, time etc.

A good hypothesis is short and clear should include the operationalized variables being investigated.

For Example

Let’s consider a hypothesis that many teachers might subscribe to: that students work better on Monday morning than they do on a Friday afternoon (IV=Day, DV=Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and on a Friday afternoon and then measuring their immediate recall on the material covered in each session we would end up with the following:

  • The experimental hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.

  • The null hypothesis states that these will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

The null hypothesis is, therefore, the opposite of the experimental hypothesis in that it states that there will be no change in behavior.

At this point you might be asking why we seem so interested in the null hypothesis. Surely the alternative (or experimental) hypothesis is more important?

Well, yes it is. However, we can never 100% prove the alternative hypothesis. What we do instead is see if we can disprove, or reject, the null hypothesis.

If we can’t reject the null hypothesis, this doesn’t really mean that our alternative hypothesis is correct – but it does provide support for the alternative / experimental hypothesis.

One tailed or two tailed Hypothesis?

A one-tailed directional hypothesis predicts the nature of the effect of the independent variable on the dependent variable.

    • E.g.: Adults will correctly recall more words than children.

A two-tailed non-directional hypothesis predicts that the independent variable will have an effect on the dependent variable, but the direction of the effect is not specified.

    • E.g.: There will be a difference in how many numbers are correctly recalled by children and adults.

How to reference this article:

McLeod, S. A. (2014). Aims and hypotheses. Retrieved from


This one-day masterclass looks at using a hypothesis testing methodology to improve the explanatory content of crime and intelligence analysis.

This approach will be illustrated with a wide range of examples: from street prostitution to drug dealing, from burglary to violent crime, from street drinking to youth-related anti-social behaviour (ASB). 

You'll follow a step-by-step guide to the hypothesis testing analysis approach and see how this can lead to:

  • producing analytical products that are more explanatory and interpretive, rather than providing only a descriptive presentation of the problem
  • improving commissioning dialogue
  • generating results that help identify more specifically how a crime problem can be tackled

The training is interactive, and will involve working on real crime issues in a classroom environment, but without the use of computers.

The course is run by UCL's Jill Dando Institute of Security and Crime Science.

Who this course is for

This course is suitable for:

  • police
  • community safety partnership (CSP) analysts
  • researchers
  • information officers

Course content

We'll discuss the current good and bad things about analysis production (its content and commissioning) and examine how a hypothesis testing approach can improve the explanatory substance of analytical materials. 

You'll then be guided through the steps of the process using existing crime (or other community safety) problems.

The following will be covered during this short course:

The hypothesis testing analysis approach

We begin by discussing the current problems with analysis and the role it should play to inform intelligence-led decision-making. We then introduce the concept of hypothesis testing and illustrate it using examples from other fields of popular science. We also discuss how a hypothesis testing analysis process can fit into existing police/CSP National Intelligence Model (NIM) processes and the problem-solving SARA process, and suggest a structure for problem profiles that use a hypothesis testing approach.

The overview

We'll look at the production of an overview, which is the first stage in the process for constructing a problem profile following the hypothesis testing approach. This involves recording key features about the problem so that it can be clearly defined. The overview is then used by key stakeholders to help them determine the main reasons why the problem exists i.e. the hypotheses.

Determining hypotheses

We'll look at how to articulate hypotheses based on the many reasons provided by stakeholders to explain the problem. We also recommend a process that helps you qualify and shortlist the hypotheses that you'll then select for directing the analysis.

Testing hypotheses

In this session we demonstrate that no extra skills or training in new techniques are required to test hypotheses - as analysts you can use your existing knowledge. This session includes identifying the data that are required for testing hypotheses, the techniques you can use use and examples of how the results of the analyses can be presented.

Interpreting and critiquing the results

In this session you'll look at how the results from hypothesis testing can be interpreted and critiqued. You'll also be shown how the results from testing each hypothesis can be brought together to provide a richer array of intelligence and evidence that helps to explain why a crime problem exists.

Writing the problem profile and review

We finish by reviewing how a problem profile can be written by following the hypothesis testing approach. We'll also identify a number of resources that provide additional reference information to help you adopt this process in the workplace.

Entry requirements

There are no formal entry requirements. The course is suitable for all levels.

Cost and concessions

There's a 10% reduction for bookings of two or more people - all group delegates must be booked at the same time.

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Course team

Course information last modified: 12 Dec 2016, 13:47