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Inferential Statistics

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.

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1

Inferential Statistics Overview and Sampling

05:25

 2

Hypothesis Testing Overview

07:49Members only

Current Video

Normality

07:36Members only

4

Next VideoCentral 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 Central Limit Theorem 

The cen­tral lim­it the­o­rem states that the dis­tri­b­u­tion of an aver­age will tend to be nor­mal as the sam­ple size increas­es, regard­less of the dis­tri­b­u­tion from which the sam­ples are tak­en. Learn how to apply this prin­ci­ple to use data that is not nor­mal­ly dis­trib­uted when con­duct­ing sta­tis­ti­cal analysis.