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This deck focuses on Evaluating Models With Data And Simulation, giving you a quick way to review the definitions, rules, and examples that matter most for Statistics.
Study Evaluating Models With Data And Simulation in 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 is the main reason to run many simulation repetitions (for example, 10,000) instead of few?
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More repetitions reduce random simulation error in the estimated probability. Law of large numbers ensures convergence to true probability.
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This deck focuses on Evaluating Models With Data And Simulation, giving you a quick way to review the definitions, rules, and examples that matter most for Statistics.
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Answer: More repetitions reduce random simulation error in the estimated probability. Law of large numbers ensures convergence to true probability.
Answer: Yes; 0.03125 0.05. The probability 0.03125 is less than the threshold 0.05.
Answer: The result is not unusually rare under the model. Events occur with expected frequency, not significantly deviating from model predictions.
Answer: The independence assumption of the probability model is violated. Dependent trials contradict binomial model's independence requirement.
Answer: Question the model if p≤0.05; otherwise do not question it. Standard threshold: reject model when probability of observed data is very low.
Answer: 321. Only one way to get all tails: TTTTT.
Answer: (21)6=641. Each flip is independent with probability 21.
Answer: np=20⋅0.5=10. Expected value formula: E[X]=np for binomial distribution.
Answer: Question the p=0.5 model because 321<0.05. The probability is below the significance threshold, suggesting model inadequacy.
Answer: inom{10}{8}(0.5)^{10}=0.0439453125. Binomial probability with n=10, k=8, p=0.5.
Answer: 1−(0.5)5=0.96875. Complement of getting all tails in 5 flips.
Answer: Yes; 0.0439453125 0.05. The probability 0.044 is less than the threshold 0.05.
Answer: (0.8)10=0.1073741824. All 10 trials fail when each has probability 0.8 of failing.
Answer: The proportion of simulations at least as extreme as observed. Measures how surprising the observed data are under the model.
Answer: The outcome is not unusually unlikely under the model. The observed result has a reasonable probability of occurring by chance.
Answer: To estimate how often a statistic occurs if the model is true. Shows the likelihood of various outcomes under the assumed model.
Answer: Using random trials to imitate a chance process under a model. Generates artificial data matching the model's probability structure.
Answer: 100037=0.037. Divide count of extreme outcomes by total simulations.
Answer: (21)5=321. Each flip has probability 21; multiply for independent events.
Answer: Treat 0-4 as heads and 5-9 as tails for each trial. Splits digits evenly to simulate fair coin with p=0.5.
Answer: Count simulations at least as far from expected in either direction. Considers deviations in both positive and negative directions.
Answer: Question the model; the data are inconsistent with it. Small p-value indicates the observed data are unlikely under the model.
Answer: Do not question the model; the data are plausible under it. Large p-value means the data could reasonably occur under the model.
Answer: The observed result would be rare if the model were true. Low probability suggests observed data unlikely under assumed model.
Answer: A probability at most 0.05. Standard significance level for hypothesis testing.
Answer: Outcomes with deviation from the model at least as large as observed. Includes all results that differ from expected by the observed amount or more.
Answer: The observed result is plausible under the model; do not question it. High probability indicates observed data aligns with model expectations.
Answer: The claimed probability distribution for the data-generating process. The hypothesis being tested about how the data are generated.
Answer: A binomial model: X∼Binomial(n,p). Models repeated independent trials with constant success probability.
Answer: (21)10=10241. Multiply individual probabilities for independent events.
Answer: An empirical p-value for the observed result under the model. Simulation frequency approximates theoretical probability of extreme events.
Answer: Do not question the model because 0.12>0.05. Exceeds significance level, so data consistent with model.
Answer: (0.5)^5=0.03125. Each flip has probability 0.5, multiply for independent events.
Answer: No; 0.1073741824>0.05. The probability 0.107 exceeds the threshold 0.05.
Answer: np=20(0.5)=10. Mean of binomial distribution is np.
Answer: Many random repetitions generated according to the model. Mimics the random process to see what typically happens.
Answer: The proportion of simulated trials at least as extreme as observed. Counts how often simulated data matches or exceeds observed extremity.