Method understanding relative risk

18% higher risk – on the relative relationship between sausage and cancer

The challenge of risk literacy

Improving risk literacy among consumers.

Risk literacy can be understood as a collection of abilities and skills that enable a critical understanding of uncertain events and the use of these in a way that is beneficial to one's own life. These abilities and skills include:

  • to correctly distinguish risk from certainty,
  • to correctly distinguish problems of risk from problems of uncertainty,
  • to correctly interpret numeric probability expressions,
  • to correctly calculate conditional probabilities,
  • to critically use graphical representations of risk (charts),
  • to question reference conditions of probability expressions,
  • to correctly distinguish between representations of absolute and relative risk.


Why is it relevant to improve consumers' risk literacy?

The aim is for the consumer to be able to make better decisions. However, when searching for information in order to prepare a decision, consumers often find statistics that promote a distorted interpretation. When percentages (individual case probabilities) are given, it is often unclear to whom and what under which conditions this number refers. Without these reference values, however, a percentage can be interpreted practically arbitrarily.

In particular, relative risk representations in the form of a percentage change make increases or decreases appear significantly stronger than they might be. An estimated 13,000 additional abortions were recorded in England in 1995 after the UK Medicines Safety Agency announced that the next generation pill would increase the risk of a dangerous blood clots by 100%. Quantifying the absolute risk - in this case the risk of a blood clot increased from one to two women out of 7,000 - could have prevented much damage.


Why is it problematic to improve risk literacy regarding the interpretation of reported probabilities? 

If consumers' risk competence regarding the interpretation of reported probabilities is to be increased, they must be encouraged to question them. Consumers should ask themselves to whom and what probability information refers. Here it is important to note:

  • Awareness that relative and absolute risks differ is not widespread.
  • The reference conditions of a single probability statement can vary, e.g. groups, periods, doses of substances.
  • The interest to engage in statistical learning contents must be sparked.
What is a suitable scientific approach?

In order to convey the various reference values of a probability statement (risk object, reference doses, reference groups, effect magnitude), it is useful to remove these from the statement step by step. In the learning visualisation, test items with explanatory feedback on the individual reference variables are therefore continuously used. These are accompanied by step-by-step changes in the visualisation. They also reflect the content-related decomposition of the individual probability statements.

In order to obtain the key information, which means a relative probability, the reader must receive two answers: How many are affected without the change (initial situation), and to which absolute quantity or absolute number does the change correspond to? Contrasting according to the fact box principle is recommended here (McDowell et al., 2016). For this, the visual form of icon arrays, the standard of scientific static risk communication, should be used. In particular, those help people who have difficulties with numbers to understand small probabilities (Galesic et al., 2009).

Interactivity can be used to nurture interest. It should be used in an economical, targeted and coherent way with the necessary controls and content. Since this is a playful transfer of knowledge from the adult world (Barth, 2018), this interest can be reinforced among certain consumers by addressing the motive of power (competition, status) (Sailer et al., 2014) by comparing their own knowledge on a certain risk with those of others. The reduced design refrains from presenting further details that motivate interest. These could contradict the actual goal, particularly if they do not fit harmoniously into the content, or even distract or simply lead to confusion (Eitel & Kühl, 2019).


Recommended literature on methodological basics
  • Barth, R. (2018). Möglichkeiten der Nutzung von Game Design Prinzipien in der Erwachsenenbildung. digital. innovativ|# digiPH, 109–118.
  • Eitel, A., & Kühl, T. (2019). Harmful or helpful to learning? The impact of seductive details on learning and instruction. Applied Cognitive Psychology, 33(1), 3–8.
  • Galesic, M., Garcia-Retamero, R., & Gigerenzer, G. (2009). Using icon arrays to communicate medical risks: Overcoming low numberacy. Health Psychology, 28, 210–216. doi:10.1037/a0014474
  • McDowell, M., Rebitschek, F. G., Gigerenzer, G., & Wegwarth, O. (2016). A simple tool for communicating the benefits and harms of health interventions: A guide for creating a fact box. MDM policy & practice, 1(1), 2381468316665365.
  • Sailer, M., Hense, J., Mandl, J., & Klevers, M. (2014). Psychological perspectives on motivation through gamification. Interaction Design and Architecture Journal, (19), 28–37.
How can you implement the method?

Option 1: You can embed the given visualisation
It is possible to embed the visualisation from our website including the frame text via iframe. To do this, use the following html-code for your website: <iframe frameborder="0" height="650px" src="" width="1024px"></iframe>

Option 2: You can adapt the given visualisation
If you use your own data as a multiplier, your web developers can enter it into your own consumer fact box with experience-based learning.

We will provide the person responsible for your website with the documented code for download via github. You can then edit the material. The link to the repository is available on request. Contact details can be found here.

Option 3: You can apply the scientific principle independently
If you require assistance, please consult the final report on the RiskAtlas project from July 2020 or contact us. Contact details can be found here.

When using the instruments, please mention the funding agency, which is the German Federal Ministry of Justice and Consumer Protection, and the Harding Centre for Risk Literacy as the responsible developers.

Logos can be downladed here.

Links to other methods

Methode Nichtlinearität verstehen

Method understanding samples

Visualisation with frame text

18% higher risk – What about sausage and cancer?

Two salami sandwiches in the morning or a sausage for lunch - according to the recommendations of the German Society for Nutrition, you should not eat more processed meat than this every day. Otherwise - and this might alarm German sausage lovers at first - your risk of colon cancer increases by 18% for every 50 grams of processed meat per day. Thus, this risk is on the same risk level as smoking or asbestos according to the World Health Organization (WHO). But before you start eating your scrambled eggs without bacon, you should ask yourself the following questions: 18% of what? How common is colon cancer? How long do you have to eat how much more processed meat than the population average to increase the risk of colon cancer by 18%? If you cannot answer any of these questions, but the statistics worry you, take a look at our interactive chart. Once you can understand exactly what relative claims such as "18% higher risk" mean and how often something normally occurs, you will feel much more relaxed.


When do I need this visualisation?

If you want to learn something. On the one hand, this learning visualisation is intended to train you in those questions with which you can break down given probabilities with regard to their reference values. This is the only way you can classify this information. On the other hand, you should also learn to question single relative risk values (percentage change) in order to understand the actual extent of change. This enhances your own risk competence. This learning visualisation is not supposed to support you in making a concrete decision.


What does the visualisation show?

The visualisation represents a sequence of multiple-choice questions. The graphical elements support the decomposition of the probability statement. In addition to the correct answers, you also receive feedback on how often other learners answered the questions correctly.

Sources and quality of the data

Where are the numbers coming from?

  • Ärzteblatt (2015). WHO-Behörde stuft rotes Fleisch und Wurst als krebserregend ein, (zuletzt abgerufen am 27.09.2019).
  • Bouvard, V., Loomis, D., Guyton, K. Z., Grosse, Y., El Ghissassi, F., Benbrahim-Tallaa, L., ... & Straif, K. (2015). Carcinogenicity of consumption of red and processed meat. Lancet Oncology, 16(16), 1599.
  • International Agency for Research on Cancer. (2015). IARC Monographs evaluate consumption of red meat and processed meat. press release, 240, (zuletzt abgerufen am 27.09.2019).

What is the quality of the data?

Die entscheidenden Zahlen zu den Krebserkrankungen stammen aus Beobachtungsstudien großer Menschengruppen (prospektive Kohortenstudien). Die Qualität solcher Studiendaten gilt grundsätzlich als moderat, da sie keinen direkten Vergleich zwischen einer zufällig ausgewählten Kontrollgruppe mit den Personen, die Fleisch verzehren, ermöglicht. 

Empirical evaluation with consumers

All research results on the fundamentals and on the effectiveness of the RiskoAtlas tools in terms of competence enhancement, information search and risk communication will be published together with the project research report on 30 June 2020. If you are interested beforehand, please contact us directly (Felix Rebitschek,

Last update: 14 Oktober 2019.

Links to other topics


Examining health information

Informed participation in bonus programs

Interessenkonflikte beim Arzt