# Lecture 5 - Statistical Power Flashcards

What are three examples of post-hoc tests?

Tukey hsd

T test

Scheffé

What does hsd stand for? As in tukey hsd test

Honestly significant difference

What does the tukey hsd test do

It establishes the smallest possible difference between two means

It uses a critical difference value. This comes from a fancy equation.

Any mean difference greater than the critical difference is significant.

How can a t test be used as a post hoc test?

It is modified - t is given by 0.05/c where c is the number of comparisons being made.

This is called a bonferroni correction

What do post hoc tests mean for error rates?

They’re conservative, which means they reduce the chance of type one errors but greatly increase the chance of a type two error

This means we can be very confident when we do find an effect

But it does mean null results are hard to interpret. There may still be an effect but we just can’t find it. (Low power??)

What are the four assumptions of the f ratio

Independence of numerator and denominator

Random sampling

Homogeneity of variance

Normality (normally distributed populations)

How can we test the assumptions of an ANOVA?

Independence and random sampling are down to the experimenter so we assume they’ve been met

But we can test homogeneity of variance and normality

How can we test for homogeneity of variance?

In a between groups design:

Hartley’s F-Max

Bartlett

Cochran’s C

Within or mixed designs:

Box’s M

How would you do Box’s M by hand?

You’d do (largest variance/smaller variance)

What are the three most common tests of normality and how do they work?

Skew

Lilliefors

Shapiro-Wilks

(The bottom two are very hard to do by hand but SPSS has them)

They compare the actual distribution of data to a model of normal distribution

They are all pretty sensitive, and more so with large samples

How do we test skew?

We can test if the skew is significantly different from 0. If everything was perfectly normally distributed skew would be 0.

We use a z-score distribution to do this

If the z score is greater than + or - 1.96 then the sample is significantly different than a normal distribution

What is a transformation and why would we use one?

Mathematical operations that we can apply to the data before we conduct an ANOVA

we use them if we don’t meet the assumptions of an ANOVA but we really want to perform one

What are the three circumstances where no transformations will make the data fit the ANOVA assumptions

Heterogenous (different) variances

Heterogenous distributions

Both of the above

What is defined as moderate, substantial and severe skew?

Moderate - 1.96-2.33

Substantial - 2.34-2.56

Severe - 2.56 and above

What transformation would you use for moderate positive skew

Square root

What transformation would you use for moderate negative skew

Square root (K-X)