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Everything You Ever wanted To Know About Statistics Dr. Andy Field
Aims and Objectives ,[object Object],[object Object],[object Object],[object Object],[object Object],Slide
The Research Process
 
Populations and Samples ,[object Object],[object Object],[object Object],[object Object]
The Only Equation You Will Ever Need Slide
A Simple Statistical Model ,[object Object],[object Object],[object Object],Slide
The Mean ,[object Object],[object Object],Slide
[object Object],[object Object],[object Object],The Mean: Example Slide  ,[object Object]
The mean as a model Slide
Measuring the ‘Fit’ of the Model ,[object Object],[object Object],[object Object],Slide
A Perfect Fit Slide  Rater Rating (out of 5)
Calculating ‘Error’ ,[object Object],[object Object],Slide
Slide
Use the Total Error? ,[object Object],Slide  Score Mean  Deviation 1 2.6 -1.6 2 2.6 -0.6 3 2.6 0.4 3 2.6 0.4 4 2.6 1.4 Total = 0
Sum of Squared Errors ,[object Object],[object Object],[object Object],[object Object],Slide
Slide  Score Mean  Deviation Squared Deviation 1 2.6 -1.6 2.56 2 2.6 -0.6 0.36 3 2.6 0.4 0.16 3 2.6 0.4 0.16 4 2.6 1.4 1.96 Total 5.20
Variance ,[object Object],[object Object],[object Object],Slide
Degrees of Freedom Slide  Sample Population 12 11 8 9 15 7 8 ?
Standard Deviation ,[object Object],[object Object],[object Object],Slide
Important Things to Remember ,[object Object],[object Object],[object Object],[object Object],[object Object],Slide
Same Mean, Different SD Slide
The SD and the Shape of a Distribution
Samples Vs. Populations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Slide
Sampling Variation
   = 10 M = 8 M = 10 M = 9 M = 11 M = 12 M = 11 M = 9 M = 10 Population M = 10
Confidence Intervals ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Slide
Slide
 
Test Statistics ,[object Object],[object Object]
One- and Two-Tailed Tests
Type I and Type II Errors ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
What does Statistical Significance Tell Us? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Effect Sizes ,[object Object],[object Object],[object Object],[object Object],PG Stats Andy Field
Effect Size Measures ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],PG Stats Andy Field
Effect Size Measures ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],PG Stats Andy Field

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Statistice Chapter 02[1]

Notas del editor

  1. One of the simplest models we use in statistics is the mean. Some of you may have trouble thinking of the mean as a model, but in fact it is because it represents a summary of data. The mean is a hypothetical value that can be calculated for any data set, it doesn’t have to be a value that is actually observed in the data set. For example, if we took five statistics lecturers and measured the number of friends that they had, we might find the following data: 1, 3, 4, 3, 2. If we take the mean number of friends, this can be calculated by adding the values we obtained, and dividing by the number of values measured:. Now, we know that it is impossible to have 2.6 friends (unless you chop someone up with a chainsaw and befriend part of their remains, which in practice has proved disturbing) so the mean value is a hypothetical value. As such, the mean is a model that we create to summarise our data.