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Inferential Statistics

Hypothesis Testing Overview

Hypoth­e­sis Test­ing is a sta­tis­ti­cal method that helps us make deci­sions and con­clu­sions about the over­all pop­u­la­tion using sam­ple data. Learn how we can apply con­cepts includ­ing sta­tis­ti­cal sig­nif­i­cance, p‑value, alpha, null hypoth­e­sis, alter­nate hypoth­e­sis, and more.

Course Videos

1

Inferential Statistics Overview and Sampling

05:25

Current Video

Hypothesis Testing Overview

07:49Members only

3

Next VideoNormality

07:36Members only

 4

Central Limit Theorem

07:25Members only

 5

The Z-Score

07:40Members only

 6

Creating Run Charts

04:56Members only

 7

1-Sample t-Test

06:34Members only

 8

2 Variances Test

08:58Members only

 9

2 Sample t-Test

07:53Members only

 10

Paired t-Test

06:30Members only

 11

1-Proportion Test

04:49Members only

 12

2 Proportions Test

06:46Members only

 13

Chi Square Test

11:09Members only

 14

1 Sample Sign Test

03:59Members only

 15

Mann-Whitney Test

04:48Members only

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Next Video Normality 

Before we run a hypoth­e­sis test, we must deter­mine whether our data are nor­mal­ly dis­trib­uted. This is essen­tial for run­ning the prop­er type of test to draw the cor­rect con­clu­sions. Learn how to use his­tograms, graph­i­cal analy­sis, the Ander­son-Dar­ling test, skew­ness, and Kur­to­sis to deter­mine the nor­mal­i­ty of data.