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# Introduction to Hypothesis Testing

This Prezi will serve as an introduction to hypothesis testing, with an emphasis on one-sample tests for the mean.
by

## Jennifer Edmonds, PhD

on 10 January 2017

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#### Transcript of Introduction to Hypothesis Testing

Hypothesis Testing
Step 1
Step 2
Step 3
1SHT Template
Define the Hypotheses
Calculate the Test Statistics
State the Conclusions
Introduction to
Define the Hypotheses
Hypothesis testing uses data to test a model
The first step in hypothesis testing is to translate your claims into hypothesis statements.
Set up a significance level to control the possible frequency of type 1 errors
alpha-level = probability of Type I errors
(typically 0.05)
If the sample value is too close to the reference value, DO NOT REJECT the null hypothesis.
The Null Hypothesis
Begin with the assumption that
the null hypothesis is true...

...similar to the notion of
innocent until proven guilty

Generally means that not enough evidence has been presented to indicate a change has taken place.
NULL the sample value is the same as I expected
ALT the sample value not the same as I expected
NULL the sample value is not bigger than I expected
ALT the sample value is bigger than I expected
…uses comparisons like ‘the same as’…
Implies equality
The type of test depends on what you want to prove…
One-Tailed vs. Two-Tailed
Implies some direction
…uses comparisons like ‘bigger’, ‘less than’, etc….
A hypothesis is a claim or assumption about a population parameter
Every statistical test tests the null hypothesis against an alternate hypothesis.
NULL vs. ALT
Every statistical test tests the
null
hypothesis against an
alternate
hypothesis.
Use this Excel template to store all of your calculations relating to your hypothesis test(s)…
Is the complement of the null hypothesis

...similar to the notion of
guilty beyond a reasonable doubt.

Is generally the hypothesis that the researcher is trying to prove (that a change has taken place, etc.)

Implies that sufficient evidence has been presented to indicate a change has taken place.
Covers the area of the population not covered in the NULL
NULL -

ALT -
remember that they are COMPLEMENTARY
NULL -

ALT -
ONE-TAILED
TWO-TAILED
Z-SCORE
PROBABILITY
...based on the Normal Distribution

Standard Normal Distribution
OBSERVED VALUES
CRITICAL VALUES
need to STANDARDIZE the data first...
Calculate the Test Statistics
State the Conclusions
...obtained from the data
...obtained from the distribution
How far is “far enough” to reject the NULL?
If the sample value is far enough away from the reference value, REJECT the null hypothesis.
The critical value creates benchmark for decision making.
Define the Hypotheses
Calculate the Test Statistics
State the Conclusions
One-Sample
Hypothesis Tests
for the Mean
Define the Hypotheses
Calculate the Test Statistics
State the Conclusions
One-Sample
Hypothesis Tests
for the Proportion
Summarize salary by team.
This Prezi was arranged by
Jennifer J. Edmonds, PhD
Wilkes University

jennifer.edmonds@wilkes.edu
null & alternative
observed & critical values
do not
reject the null?
develop a visual to
let the data guide you...what do you see?
what you see in the data
NULL the sample value is the same as I expected
ALT
NULL
ALT
this
sample value is bigger than
that
sample value
The type of test depends on what you want to prove…
One-Sample vs. Two-Sample
what you see in the data
NULL
ALTERNATIVE
NULL
ALT the sample value is bigger than I expected
a performance standard
a performance standard
a reference value
a reference value
average highway MPG=28.08
compares one sample to a performance standard
compares two samples to one another
pick ONE sample
and compare it to the performance 'standard'
NULL
this
sample value is the same as
that
sample value
ALT
pick TWO samples
and compare their highway fuel efficiency
Full transcript