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(Random) (2.A) Statistical process sampling and experimentation. Compare and contrast the benefits of different sampling techniques, including random sampling and convenience sampling methods;

(Random) (2.B) Statistical process sampling and experimentation. Distinguish among observational studies, surveys, and experiments; and (2.C) Analyze generalizations made from observational studies, surveys, and experiments;

(Random) (2.E) Statistical process sampling and experimentation. Formulate a meaningful question, determine the data needed to answer the question, gather the appropriate data, analyze the data, and draw reasonable conclusions;

(Random) (2.F) Statistical process sampling and experimentation. Communicate methods used, analyses conducted, and conclusions drawn for a data-analysis project through the use of one or more of the following: a written report, a visual display, an oral report, or a multi-media presentation; and

(Random) (2.G) Statistical process sampling and experimentation. Critically analyze published findings for appropriateness of study design implemented, sampling methods used, or the statistics applied.

(Random) (4.C) Categorical and quantitative data. Analyze the distribution characteristics of quantitative data, including determining the possible existence and impact of outliers;

(Random) (5.B) Probability and random variables. Describe the relationship between theoretical and empirical probabilities using the Law of Large Numbers;

(Random) (5.C) Probability and random variables. Construct a distribution based on a technology-generated simulation or collected samples for a discrete random variable; and

(Random) (5.D) Probability and random variables. Compare statistical measures such as sample mean and standard deviation from a technology-simulated sampling distribution to the theoretical sampling distribution.

(Random) (6.B) Inference. Explain how changes in the sample size, confidence level, and standard deviation affect the margin of error of a confidence interval;

(Random) (6.H) Inference. Explain the meaning of the p-value in relation to the significance level in providing evidence to reject or fail to reject the null hypothesis in the context of the situation;

(Random) (6.I) Inference. Interpret the results of a hypothesis test using technology-generated results such as large sample tests for proportion, mean, difference between two proportions, and difference between two independent means; and

(Random) (7.F) Bivariate data. Identify and interpret the reasonableness of attributes of lines of best fit within the context, including slope and y-intercept.