To send content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about sending content to .
To send content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Careful attention to ways in which information is communicated and represented can have a significant impact on what decisions will be taken, in a way that has implications for many different professions. For example, journalists can adopt strategies for reporting disasters that facilitate causal analysis and coping in the public, or methods for representing statistical data that enable the public to properly evaluate the validity of politicians’ claims. Doctors can adopt ways of talking about uncertain treatment outcomes that will either encourage or discourage a course of action by a patient. Science teachers and statisticians can adopt graphical formats that enable complex relationships to be better understood by students and the general public. Indeed, as we will show, improving the communication and representation of information that will serve as input into a decision-making process can act as a powerful “scaffold” for optimizing human decision making.
Effective design of communication with the human decision maker depends on the characteristics of the communication channel used (spoken language, verbal text, graphical representation, etc.), and of the information-processing abilities of the recipient. We begin by noting several general features of human communication and information processing that condition the way communications should be designed, before moving on to note some particular ones. We then proceed to give examples that illustrate the importance of taking these features into account. After that, we will demonstrate how the way information is communicated can have a strong impact on judgments about causality; especially in designing external representations such as graphs, one has options about what and how much information to put in, and what to make salient. The issue of external representation becomes even more important when communicating about probabilities, which we will focus on afterwards. The general idea advanced in this chapter is that appropriate ways of communication can be used as a “scaffold” to optimize judgment and decision making: communicators can help decision makers. We will give examples of scaffolding throughout the chapter and will summarize the most important recommendations at the end of it.
Suppose a state election is coming up in a certain European country and somebody asks you if you would bet on whether the Social Democrats will beat the Christian Democrats again as they did in the last election. Shortly before Election Day, you are presented the results of two opinion polls, based on random samples of either 100 or 1,000 voters. In the smaller sample, the Social Democrats have an advantage of 4 percentage points whereas in the larger sample, the Christian Democrats lead by the same amount. Would you take the bet? You probably would not and I assume that this would be the choice of most people asked this question. As we will see later, your choice would be justified on both empirical and theoretical grounds.
This chapter offers an explanation for how and when the “naïve intuitive statistician” (Fiedler & Juslin, this volume) understands the impact that the size of a sample should have on estimates about population parameters such as proportions or means. I will argue that, in general, people know intuitively that larger (random) samples yield more exact estimates of population means and proportions than smaller samples and that one should have more confidence in these estimates the larger the size of the sample. My main claim is that the size-confidence intuition – an intuition that properly captures the relationship between sample size and accuracy of estimates – governs people's judgments about sample size. This intuition can be regarded as the result of associative learning.
Email your librarian or administrator to recommend adding this to your organisation's collection.