Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.

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來自 Johns Hopkins University 的課程

Mathematical Biostatistics Boot Camp 2

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Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.

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Hypothesis Testing

In this module, you'll get an introduction to hypothesis testing, a core concept in statistics. We'll cover hypothesis testing for basic one and two group settings as well as power. After you've watched the videos and tried the homework, take a stab at the quiz.

- Brian Caffo, PhDProfessor, Biostatistics

Bloomberg School of Public Health

Hi.

My name is Brian Caffo, and this is Mathematical

Biostatistics Boot Camp 2: Lecture 1, on Hypothesis Testing.

So in this lecture we'll talk about hypothesis testing.

We'll go over some general rules for hypothesis

tests, mostly in the context of one-sided hypothesis tests.

We'll cover briefly two sided hypothesis test.

We show that there's a duality and a connection between hypothesis

test and confidence intervals. And then we'll briefly cover p values.

Hypothesis testing is concerned with making decisions using data.

A null hypothesis is specified that represents the status quo.

Usually, this is labeled h not.

The null hypothesis is assumed true.

And statistical evidence is required to reject it

in favor of a research or alternative hypothesis.

So one might think of this in terms of a court of law.

Where a person is presumed innocent until evidence in presented to convict them.

In this case, the null hypothesis is presumed true, and then,

evidence is required to, in this case, convict it or reject it.