Scientific Decision-Making (AQA A Level Business) : Revision Note

Making decisions using data

• Scientific decision making involves using data to make rational, logic-based decisions

  • This method lowers risk, but it isn’t perfect—good data cost money and numbers never tell the whole story

What the scientific approach involves

1. Set the objective

   • e.g. Increase sales of gym accessories to under‑25s by 5 % by November 2025

2. Collect data

   • A range of internal and external sources can provide useful insights

   • Internal data, such as

     • Sales and loyalty‑card records

     • Production logs

     • Finance systems

     • Website analytics

   • External data, such as

     • Government statistics

     • Industry reports

     • Social media trends

     • Customer reviews

3. Analyse data and select an option

   • Use statistics, A/B tests or forecasts to determine the best options, then make a choice with the strongest evidence

4. Implement the decision

   • Put in place the resources required, such finance, staff, equipment and premises

5. Review and learn

   • Compare outcomes with the original goal; make necessary changes to keep improving

Stages in the decision-making process

Scientific decision-making at Tesco

Benefits of scientific decision-making

• Reduces risk

  • Decisions rest on evidence, not guess‑work

  • Firms using reliable data are more likely to report better outcomes

• Justifies investment

  • Clear numbers help win the support of a board of directors, investors or lenders such as banks

• Supports continuous improvement

  • Constant measurement helps a business to identify what works and what may need to be changed

Limitations of scientific decision-making

• Cost and time

  • Gathering and analysing data is expensive

  • It may be unaffordable for smaller businesses or those with a poor cash situation

• Data quality issues

  • Bad or biased data can lead to wrong or inappropriate decisions being made

• Over‑reliance

  • Managers can ignore gut feel or ethics

  • They may miss out on opportunities that have a good chance of success because the data does not recognise their potential

• Incomplete picture

  • Not all risks are measurable and relying on data means that businesses can miss surprises such as rapid market change

Intuition and decision-making

• Managers sometimes rely on gut feel, experience and pattern‑spotting rather than detailed data analysis to choose a course of action

  • Experienced managers build quick mental shortcuts from years of experience, so their gut instantly signals, “This feels right”

Situations where intuitive decision-making may work best

Benefits of intuitive decision-making

• Speed

  • Rapid action can allow a business to seize opportunities before rivals can react

• Creativity

  • It frees managers to pursue bold ideas that data might reject

• Uses deep expertise

  • Experienced managers are based on past successes, failures and patterns

Limitations of intuitive decision-making

• Bias and overconfidence

  • Personal likes or recent events can cloud judgement

• Hard to justify

  • Convincing investors or lenders without data to back up ideas can be difficult

• Riskier on big bets

  • A wrong hunch can be very costly, so managers risk their personal reputation in pursuing them