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## PowerPoint Slideshow about ' I. Statistical Tests:' - carlow

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### I. Statistical Tests:

### I. Statistical Tests: (cont.)

### II. How to do Statistical Tests?

### II. How to do Statistical Tests? (cont.)

### II. How to do Statistical Tests? (cont.)

### III. What is ANOVA?

### C. What is ANOVA? (cont.)

### C. What is ANOVA? (cont.)

### C. What is ANOVA? (cont.)

### C. What is ANOVA? (cont.)

### C. The Logic of ANOVA (continued)

Why do we use them?

Namely: we need to make inferences from incomplete information or uncertainty

But we want them to be “educated” or “calculated” guesses

We want to make them with some minimal likelihood of a wrong inference (or conversely, a maximal chance of a correct inference).

Note: If we had full information, we wouldn’t need statistical tests – in fact, they would be meaningless.

What do they involve?

The “Null Hypothesis Testing” procedure

What is the underlying logic of NHT?

What do they involve?

The basic logic of testing a “null hypothesis”?

Testing our research hypothesis against a null hypothesis (any differences found = sampling error)

Compare sample value against value specified by Null-Hypothesis (considering sampling variability)

Are the difference so large that it is unlikely to be sampling error (considering sampling variability)?

The form of the research hypothesis determines the “directionality” of the test

Non-directional hypothesis = 2-tailed test

Directional hypothesis = 1-tailed test

2-tailed test = more conservative

1-tailed test = more likely to find significant result

I. Statistical Tests: (cont.)

- What makes a test result turn out “statistically significant”?
- Magnitude of the effect or difference
- Amount of variability in data
- Sample size
- Probability level selected (“alpha level”)
- Directional vs. non-directional test
- Number of total tests performed

3 Basic Tasks: Testing hypotheses about:

1 mean (or 1 sample)

2 means (or 2 samples)

3 or more means (or samples)

One Sample tests

Testing a sample statistic against a hypothetical value

Relevant when we have a specific prediction about the mean(rarely)

This is the simplest form of statistical test

Use a Z or t test here (whichever is relevant)

Two Sample tests

Test difference between two means or groups (against Null-H that they are equal)

Difference between 2 independent means

Compare 2 separate groups on 1 variable

Difference between 2 correlated or paired means (also: “matched groups” or “repeated measures”)

Compare 2 variables on 1 group

Use Z-test when population variance is known

Use t-test when population variance is unknown and must be estimated from the sample

What are the assumptions of these tests?

Test of 1 Mean = “1 Sample T-test”

Test of 2 Separate Means: “Indep. Samples T-Test”

Test of 2 Correlated Groups = “Paired Samples T-test”

Tests with 3 or groups (& means)?

Why not calculate multiple pair-wise tests?

Can become unwieldy if we have no specific predictions about the pattern

Overall error rate increases rapidly as the number of tests increases

Note: per-test vs overallerror rates

The Solution = Use an overall statistical test of differences among multiple (2 or more) groups This is called “Analysis Of Variance” (or commonly called ANOVA)

Similar in logic to t-test comparison of two means except that we use squared deviations rather than simple differences

Derive two independent estimates of the population variance for the scores

One based on the variations among the group means (between-group estimate)

One based on the variations among scores in each of the groups (within-group estimates) (pooled)

Each group is taken as a random sample from the same population (under the null hypothesis)

Compare the two separate variance estimates (between-group vs. within-group)

Compute a ratio of the two estimates:

(between means) / (within groups)

If the variation in the group means is larger than the variations within the separate groups, then the groups are really different (i.e., they are not from the same population)

Use a statistic that is computed as the ratio of two variances called the F statistic

has a calculated probability distribution

F = (Variance of Means)/(Variance of scores)

F-distribution depends on two parameters:

These represent the “degrees of freedom” in the two different variance estimates in the ratio

df1 = degrees of freedom in numerator (group means) k - 1

df2 = degrees of freedom in individual scores N - k

The logic of the F-test?

If the F-ratiois much larger than 1.0, which means much larger differences between group means than expected from random sampling errors

Then we reject the null hypothesis of equality of means

This decision is based on the probability distribution of the F-statistic so that Type 1 error is .05 or less.

The null hypothesis and the alternative hypothesis of the ANOVA F-test?

H0:

Hr:

(for at least 2 groups)

What does the F-test tell us?

The F-test is a non-directional, omnibus test

The F-test doesn’t tell us which two means are different or in what direction

It simply affirms that we can say that “at least 2 group means are different” (with a .95 confidence level)

What if we want to be more specific?

Can use inspection-by-eyeball method

Can use post-hoc comparisons (or contrasts)

“Post Hoc Comparisons” identify which pairs of means are significantly different, while

Control overall error rate of the whole set of comparisons

Adjust each individual pairwise comparison to keep the overall error rate at desired level (e.g., .05)

Which “post hoc comparison” procedure to use?

Many different procedures (and frameworks) have been developed and are available in SPSS

In this class, select only one: Tukey’s HSD procedure (in SPSS, is simply labeled Tukey)

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