Make data-driven decisions! Learn to analyze statistical data, interpret results, and use evidence to make informed choices in real-world situations.
• Identify the question or problem
• Determine what data is needed
• Choose appropriate sampling method
• Ensure data quality and reliability
• Calculate statistical measures
• Identify patterns and trends
• Look for outliers and anomalies
• Compare different data sets
• Understand what the data means
• Consider context and limitations
• Identify relationships and correlations
• Draw meaningful conclusions
• Use evidence to support decisions
• Consider multiple options
• Evaluate risks and benefits
• Communicate findings clearly
Problem: A school wants to decide which teaching method is more effective. They test Method A with 30 students and Method B with 30 students. Here are the results:
Method A Results:
Mean: 78, Median: 80, Range: 25, Standard Deviation: 8.2
Method B Results:
Mean: 82, Median: 85, Range: 20, Standard Deviation: 6.5
Analysis:
Decision: Recommend Method B for implementation
Scenario: You have $1000 to invest. Option A has 70% chance of 20% return, 30% chance of -10% loss. Option B has 60% chance of 15% return, 40% chance of -5% loss.
Expected Value Calculation:
Option A: 0.7 × 200 + 0.3 × (-100) = 140 - 30 = $110
Option B: 0.6 × 150 + 0.4 × (-50) = 90 - 20 = $70
Risk Analysis:
Decision Framework:
If you prefer higher returns and can accept risk: Choose A
If you prefer stability and lower risk: Choose B
Process:
1. State hypothesis 2. Collect data 3. Test significance
Use: Making decisions based on statistical evidence
Definition:
Range of values with specified confidence level
Use: Estimating population parameters from sample data
Market Research
Customer preference analysis
Product launch decisions and pricing strategies
Clinical Research
Treatment effectiveness studies
Public health policy and disease prevention
Machine Learning
Model validation and performance
A/B testing and user experience optimization