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This deck focuses on The Geometric Distribution, giving you a quick way to review the definitions, rules, and examples that matter most for AP Statistics.
Study The Geometric Distribution in AP Statistics with focused flashcards that help you recognize the idea, recall the key rule, and apply it in practice-style prompts.
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What does the geometric distribution assume about trial independence?
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Trials are independent. Each trial outcome doesn't affect subsequent trial probabilities.
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This deck focuses on The Geometric Distribution, giving you a quick way to review the definitions, rules, and examples that matter most for AP Statistics.
Work through these flashcards in short sessions. Try to answer each prompt before flipping the card, then revisit any cards you miss until the explanation feels automatic.
Answer: Trials are independent. Each trial outcome doesn't affect subsequent trial probabilities.
Answer: Discrete random variable. Counts whole number trials, not continuous values.
Answer: The constant probability p. Success probability remains fixed across all trials.
Answer: Positively skewed. Most probability mass at X=1, decreasing for higher values.
Answer: As p increases, the mean decreases. Higher p means success comes sooner on average.
Answer: Var(X)=p21−p. Standard variance formula derived from geometric distribution theory.
Answer: P(X≤5)=1−(0.8)5. CDF calculation for success within 5 trials.
Answer: E(X)=p1. Higher success probability means fewer expected trials needed.
Answer: P(X≤4)=1−(0.7)4. Uses CDF formula with success probability p=0.3.
Answer: P(X≤k)=1−(1−p)k. CDF gives probability of success by trial k.
Answer: P(X>3)=(1−0.2)3. Probability of needing more than 3 trials equals (0.8)3.
Answer: E(X)=0.251=4. Expected value calculation using mean formula.
Answer: Var(X)=0.421−0.4. Uses standard variance formula p21−p with p=0.4.
Answer: P(X=4)=(0.7)3×0.3. Three failures (1−0.3)3 then success 0.3 on trial 4.
Answer: Probability of success p. Single parameter p completely specifies the distribution.
Answer: P(X≤3)=1−(0.5)3. CDF formula gives probability of success by trial 3.
Answer: P(X≤5)=1−(0.8)5. CDF calculation for success within 5 trials.
Answer: The number of trials until the first success. Counts trials needed to achieve one success in independent trials.
Answer: Variance decreases. Higher success probability reduces variability in waiting time.
Answer: Trials are independent with constant probability p of success. Each trial must have same success chance and be unaffected by others.
Answer: P(X>k)=(1−p)k. Survival function giving probability of exceeding k trials.
Answer: P(X≤3)=1−(0.5)3. CDF formula gives probability of success by trial 3.
Answer: E(X)=0.151. Mean equals p1 for geometric distribution.
Answer: 0.41−0.4. Standard deviation equals p21−p.
Answer: Geometric is a special case of negative binomial with r=1. Negative binomial generalizes to r successes; geometric has r=1.
Answer: Memoryless property. Previous failures don't influence future trial outcomes.
Answer: P(X≤k)=1−(1−p)k. CDF gives probability of success by trial k.
Answer: As p increases, the mean decreases. Higher p means success comes sooner on average.
Answer: P(X=k)=(1−p)k−1p. Standard PMF where k is trial number and p is success probability.
Answer: P(X=3)=(0.6)2×0.4. Two failures (0.6)2 followed by success 0.4.
Answer: P(X=2)=(1−0.25)1×0.25. One failure (0.75)1 then success 0.25 on trial 2.
Answer: Var(X)=0.221−0.2. Standard variance formula applied with given probability.
Answer: Mode = 1. First trial always has highest probability of success.
Answer: Memoryless property. Previous failures don't influence future trial outcomes.
Answer: Yes. Past failures don't affect future success probabilities.
Answer: Mode = 1. First trial always has highest probability of success.
Answer: P(X=4)=(0.7)3×0.3. Three failures (1−0.3)3 then success 0.3 on trial 4.
Answer: Geometric is a special case of negative binomial with r=1. Negative binomial generalizes to r successes; geometric has r=1.
Answer: P(X=3)=(0.6)2×0.4. Two failures (0.6)2 followed by success 0.4.
Answer: P(X>5)=(0.7)5. Complement of CDF gives probability of exceeding 5 trials.
Answer: P(X>3)=(1−0.2)3. Probability of needing more than 3 trials equals (0.8)3.
Answer: P(X=k)=(1−p)k−1p. Standard PMF where k is trial number and p is success probability.
Answer: Decreasing exponential shape. Highest probability at X=1, decreasing exponentially thereafter.
Answer: Trials are independent. Each trial outcome doesn't affect subsequent trial probabilities.
Answer: E(X)=0.151. Mean equals p1 for geometric distribution.
Answer: Var(X)=0.421−0.4. Uses standard variance formula p21−p with p=0.4.
Answer: P(X=2)=(1−0.25)1×0.25. One failure (0.75)1 then success 0.25 on trial 2.
Answer: Bernoulli trials. Independent trials with constant success probability.
Answer: Bernoulli trials. Independent trials with constant success probability.
Answer: The number of trials until the first success. Counts trials needed to achieve one success in independent trials.
Answer: Number of failures before the first success. Alternative interpretation: failures before achieving success.
Answer: E(X)=0.51=2. Expected trials equals reciprocal of success probability.
Answer: E(X)=0.51=2. Expected trials equals reciprocal of success probability.
Answer: Probability of success p. Single parameter p completely specifies the distribution.
Answer: E(X)=0.251=4. Expected value calculation using mean formula.
Answer: Var(X)=0.221−0.2. Standard variance formula applied with given probability.
Answer: P(X≤4)=1−(0.7)4. Uses CDF formula with success probability p=0.3.
Answer: Number of failures before the first success. Alternative interpretation: failures before achieving success.
Answer: P(X=1)=0.6. Success on first trial occurs with probability p.
Answer: Modeling the number of trials until the first success. Primary use case in probability and statistics applications.
Answer: P(X=1)=0.6. Success on first trial occurs with probability p.
Answer: Decreasing exponential shape. Highest probability at X=1, decreasing exponentially thereafter.
Answer: P(X>k)=(1−p)k. Survival function giving probability of exceeding k trials.
Answer: Yes. Past failures don't affect future success probabilities.
Answer: Modeling the number of trials until the first success. Primary use case in probability and statistics applications.
Answer: E(X)=p1. Higher success probability means fewer expected trials needed.
Answer: Var(X)=p21−p. Standard variance formula derived from geometric distribution theory.
Answer: The geometric distribution. Specifically models waiting time until first success occurs.
Answer: Variance decreases. Higher success probability reduces variability in waiting time.
Answer: Positively skewed. Most probability mass at X=1, decreasing for higher values.
Answer: Trials are independent with constant probability p of success. Each trial must have same success chance and be unaffected by others.
Answer: The constant probability p. Success probability remains fixed across all trials.
Answer: Discrete random variable. Counts whole number trials, not continuous values.
Answer: The geometric distribution. Specifically models waiting time until first success occurs.