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\nHow P-Values Help Us Test Hypotheses - Crash Course Statistics\n

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\n\nPublisher:\n\nCrash Course\n\n\n
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\nToday we're going to begin our three-part unit on p-values. In this episode we'll talk about Null Hypothesis Significance Testing (or NHST) which is a framework for comparing two sets of information. In NHST we assume that there is no...\nToday we're going to begin our three-part unit on p-values. In this episode we'll talk about Null Hypothesis Significance Testing (or NHST) which is a framework for comparing two sets of information. In NHST we assume that there is no difference between the two things we are observing and and use our p-value as a predetermined cutoff for if something seems sufficiently rare or not to allow us to reject that these two observations are the same. This p-value tells us if something is statistically significant, but as you'll see that doesn't necessarily mean the information is significant or meaningful to you.\n
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Resource Details

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Educator Rating
Not yet Rated
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Media Length
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11:10
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Grade
12th - Higher Ed
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Subjects
Math
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Media Type
Instructional Videos
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Source:
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\nCrash Course Statistics\n\n
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Date
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2018
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Language
English
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Audiences
For Teacher Use
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Usage Permissions
Fine Print: Educational Use
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Concepts

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population, data, evidence, giraffes, caloric intake, standard deviation, statistics
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Additional Tags

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mozart, number, mathematics, friends, average number, true population, extreme, random variation, means, extreme values, random sample, sufficient evidence, sample, friends model, random chance, experiments, models, sample means, null distribution, average, regular population, science, null hypothesis, calories eaten, calculus grades, hypothesis, baby giraffes, null, maths, distribution
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Classroom Considerations

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Best For: Explaining a topic
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