Reliability of the Data Collected
→ Reliability → how accurate and trustworthy the data is
→ Factors affecting reliability:
→ source of data → reputable sources are more reliable
→ sample size → larger samples increase accuracy
→ sampling method → unbiased methods improve validity
→ date of data → recent data is more relevant
→ method of collection → well-designed questionnaires reduce errors
→ Improving reliability:
→ use large and representative samples
→ avoid biased or leading questions
→ cross-check data using different sources
Analysis of Quantitative and Qualitative Data
→ Quantitative data:
→ numerical data (e.g. sales figures, percentages)
→ easy to measure and compare
→ often presented in charts, graphs, tables
→ Qualitative data:
→ descriptive data (e.g. opinions, preferences)
→ provides deeper insights into customer behaviour
→ collected through interviews, focus groups
→ Comparison:
→ quantitative → objective, easier to analyse
→ qualitative → subjective, richer detail
Interpretation of Tables, Charts and Graphs
→ Tables:
→ show detailed numerical data
→ useful for precise comparison
→ Charts and graphs:
→ visual representation of data
→ easier to identify trends and patterns
→ Key skills in interpretation:
→ identify trends → increase, decrease, fluctuations
→ compare values → highest, lowest, differences
→ calculate changes → percentages, growth rates
→ draw conclusions → what the data suggests for the business
Example of Data Interpretation
→ Product B has the highest sales → most popular product
→ Product C has the lowest sales → may need improvement or promotion
→ Business may increase production of Product B → to maximise revenue
Analysis
→ reliable data → leads to better decision-making
→ quantitative data → helps identify trends → supports forecasting
→ qualitative data → explains reasons behind trends → improves strategy
Evaluation
→ quantitative data is easier to analyse → but may lack depth
→ qualitative data provides insight → but may be biased or hard to measure
→ reliability is crucial → poor data leads to incorrect decisions
→ best approach → combine both types of data → for accurate and meaningful conclusions