Scientific Decision Making

Test yourself on Scientific Decision Making

Test your knowledge with free interactive questions on Seneca — used by over 10 million students.

Decision Making

Decisions can be made using different approaches. Either using scientific data, or based upon past experiences.

Methods to make decisions

When decisions are made, managers can use different approaches to support them:
- Decisions can be made scientifically using data available to the manager.
- Decisions can be made based upon intuition and the experiences of a manager.

Advantages of scientific decision making

Scientific decision making reduces the risk of making mistakes as decisions are based on data.
Another advantage is that using data provides guidance for managers who may have limited intuition through lack of experience.

Disadvantages of scientific decision making

Scientific decision making can be time-consuming to collect the data required.
Relying on data may mean that the experience or expertise of staff may not be considered.
Using out of date or poor data is unreliable and can affect the quality of the decision made.

Factors influencing decision making

The availability and reliability of data are essential if a manager chooses to use scientific decision making. If data is not available, the scientific decision-making approach cannot be used.
The manager’s experience is vital, as inexperienced managers are more likely to use scientific decision making because they have no experience or expertise.
The risk that a business is willing to accept is crucial. Scientific decision making typically carries less risk so suits businesses that are less willing to accept risk.

Decision trees

If managers choose to adopt a scientific decision making approach, they can use a decision tree to help them. A decision tree allows a business to compare outcomes of two or more options or decisions.
A decision tree will examine the probability of each outcome for each decision made.
A decision tree will multiply the probability with each outcome to calculate an estimated value (EV) for each option or decision being considered.

Example of Decision Making

Imagine a restaurant is deciding whether to expand (option 1) or open a new location (option 2). Here, a decision tree can be used to examine the range of possible outcomes.

$Option 1$

Option 1

In option 1, there may be a 70% chance of receiving a £10,000 pay-off or a 30% chance of a £20,000 pay-off.
To calculate the EV for option 1:
- Multiply 0.70 (probability expressed as a decimal) by £10,000 to get £7,000.
- Multiply 0.30 (probability expressed as a decimal) by £20,000 to get £6,000.
Using these probabilities and outcomes, the estimated value (EV) of option 1 is £13,000.

Option 2

In option 2, there may be a 20% chance of receiving a £7,000 pay-off or an 80% chance of a £15,000 pay-off.
To calculate the EV for option 2:
- Multiply 0.20 (probability expressed as a decimal) by £7,000 to get £1,400
- Multiply 0.80 (probability expressed as a decimal) by £15,000 to get £12,000
- Using these probabilities and outcomes, the estimated value (EV) of option 2 is £13,400

Conclusion

Comparing estimated values for option one and two, scientifically it can be seen that option 2 is probably the best course of action for the manager to take based on the data available.