Calculate the chance of obtaining extreme results under the null hypothesis. If p-value < α (e.g., 0.05), there's less than a 5% chance it occurred by chance.

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Confidence intervals offer parameter ranges with confidence levels, crucial in medical research. Hypothesis testing validates or refutes chance occurrences, fundamental for research.

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Z-tests use a z-statistic with known variance or large samples. T-tests, with t-statistic, apply to unknown variance & small samples. Choose based on these factors.

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Use Linear for continuous variables assuming a linear relationship; Logistic for binary outcomes, predicting probabilities with a curve. Different distributions, assumptions, & use cases set them apart.

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Randomized experiment comparing variants A and B to determine performance. Widely used in marketing, e.g., comparing email CTAs to improve customer engagement.

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Understand Simple Random, Systematic, Convenience, Cluster, and Stratified sampling methods with examples. Choose the appropriate technique based on study needs.

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Sample means distribution approximates a normal distribution, crucial for understanding population characteristics. Larger samples enhance accuracy & reduce standard deviation.

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Permutations consider order, while combinations do not, vital for fields like network security and operations research. Equations and examples illustrate their application.

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Bayes Theorem calculates the probability of B given A, fundamental for Naïve Bayes in machine learning. Common in online machine learning algorithms.

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Explore Normal, Poisson, & Uniform distributions for assessing probabilities in experiments. Eg: Bell-shaped Normal distribution & discrete Poisson distribution for events in fixed time intervals.

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