This lecture is based on the Minitab Blog series.
The presentation is prepared with R Markdown.
Navigate the presentation with arrow keys. Press o
to switch to the overview.
The source code is available on GitHub.
2021-05-05
This lecture is based on the Minitab Blog series.
The presentation is prepared with R Markdown.
Navigate the presentation with arrow keys. Press o
to switch to the overview.
The source code is available on GitHub.
A hypothesis test evaluates two mutually exclusive statements about a population to determine which statement is best supported by the sample data.
How well a sample statistic estimates an underlying population parameter?
Sample mean |
---|
5.129893 |
5.290907 |
4.468820 |
5.831213 |
5.130247 |
5.737346 |
How unusual is our new sample mean? Let’s set various thresholds for unusuality.
The shaded region indicates the probability of finding the sample mean FI.
This new measurement seems quite different from the population mean (=5).
The thresholds for shaded regions determine how far away our sample statistic must be from the null hypothesis value before we can say it is unusual enough to reject the null hypothesis.
Sample |
---|
4.273791 |
5.064480 |
4.419903 |
3.749903 |
5.279963 |
From Khan Academy:
A baseball coach was curious about the true mean speed of fastball pitches in his league. The coach recorded the speed in kilometers per hour of each fastball in a random sample of 100 pitches and constructed a 95%, percent confidence interval for the mean speed. The resulting interval was (110,120).
We’re 95% confident that the interval (110,120) captured the true mean pitch speed.
The significance level defines the distance the sample mean must be from the null hypothesis to be considered statistically significant.
The confidence level represents the percentage of intervals that would include the population parameter if you took samples from the same population again and again.
What are type I and type II errors?
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