Key Takeaways
Lesson Description
Key Takeaways
• Statistics uses data to describe populations and make informed decisions.
• Descriptive statistics summarize data using graphs and numerical measures.
• Probability provides the foundation for statistical inference.
• Sampling distributions explain why sample statistics vary.
• Confidence intervals estimate unknown population parameters.
• Hypothesis tests evaluate evidence using sample data.
• Regression and correlation describe relationships between quantitative variables.
• Chi-square procedures analyze categorical data.
• Always interpret statistical conclusions in the context of the problem.
Must Know Equations
Mean:
x̄ = Σx/n
Standard Deviation:
s = √[Σ(x−x̄)²/(n−1)]
Regression Line:
ŷ = a + bx
Addition Rule:
P(A∪B)=P(A)+P(B)−P(A∩B)
Multiplication Rule:
P(A∩B)=P(A)P(B)
(Independent Events)
Expected Value:
μ = Σxp(x)
Sampling Distribution:
σx̄ = σ/√n
Confidence Interval:
Statistic ± Critical Value × Standard Error
Chi-Square:
χ² = Σ[(O−E)²/E]
Must Know Vocabulary
Population
Sample
Parameter
Statistic
Mean
Median
Standard Deviation
Correlation
Regression
Probability
Random Variable
Sampling Distribution
Confidence Interval
Hypothesis Test
Null Hypothesis
Alternative Hypothesis
p-value
Chi-Square
AP Exam Focus
• Describe distributions using SOCS (Shape, Outliers, Center, Spread).
• Interpret graphs, regression output, and statistical summaries.
• Construct and interpret confidence intervals.
• Perform hypothesis tests correctly.
• Explain statistical conclusions using context, not just calculations.
• Distinguish association from causation.