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Major Points. Formal Tests of Mean Differences Review of Concepts: Means, Standard Deviations, Standard Errors, Type I errors New Concepts: One and Two Tailed Tests Significance of Differences . Important Concepts. Concepts critical to hypothesis testing Decision Type I error

Major Points

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- Formal Tests of Mean Differences
- Review of Concepts: Means, Standard Deviations, Standard Errors, Type I errors
- New Concepts: One and Two Tailed Tests
- Significance of Differences

- Concepts critical to hypothesis testing
- Decision
- Type I error
- Type II error
- Critical values
- One- and two-tailed tests

- When we test a hypothesis we draw a conclusion; either correct or incorrect.
- Type I error
- Reject the null hypothesis when it is actually correct.

- Type II error
- Retain the null hypothesis when it is actually false.

- Type I error

- Assume there are no differences (null hypothesis is true)
- Assume our results show that they are not same (we reject null hypothesis)
- This is a Type I error
- Probability set at alpha ()
- usually at .05

- Therefore, probability of Type I error = .05

- Probability set at alpha ()

- Assume there are differences (alternative hypothesis is true)
- Assume that we conclude they are the same (we accept null hypothesis)
- This is also an error
- Probability denoted beta ()
- We can’t set beta easily.
- We’ll talk about this issue later.

- Probability denoted beta ()
- Power = (1 - ) = probability of correctly rejecting false null hypothesis.

- These represent the point at which we decide to reject null hypothesis.
- e.g. We might decide to reject null when (p|null) < .05.
- Our test statistic has some value with p = .05
- We reject when we exceed that value.
- That value is the critical value.

- Two-tailed test rejects null when obtained value too extreme in either direction
- Decide on this before collecting data.

- One-tailed test rejects null if obtained value is too low (or too high)
- We only set aside one direction for rejection.

- One-tailed test
- Reject null if number of red in Halloween candies is higher

- Two-tailed test
- Reject null if number of red in Halloween candies is different (whether higher or lower)

- Related samples
- Difference scores
- t tests on difference scores
- Advantages and disadvantages

- The same participant / thing give us data on two measures
- e. g. Before and After treatment
- Usability problems before training on PP and after training
- Darts and Pros during same time period

- With related samples, someone high on one measure probably high on other(individual variability).

Cont.

- Correlation between before and after scores
- Causes a change in the statistic we can use

- Sometimes called matched samples or repeated measures

- Calculate difference between first and second score
- e. g. Difference = Before - After

- Base subsequent analysis on difference scores
- Ignoring Before and After data

- Pros got more gains than darts
- Was this enough of a change to be significant?

- If no difference, mean of computed differences should be zero
- So, test the obtained mean of difference scores against m = 0.
- Use same test as in one sample test

D and sD = mean and standard deviation of differences.

df = 100 - 1 = 9 - 1 = 8

Cont.

- With 99 df, t.01 = +2.62 (Table E.6)
- We calculated t = 2.64
- Since 6.64 > 2.62, reject H0
- Conclude that the Pros did get significantly more than Darts

- Eliminate subject-to-subject variability
- Control for extraneous variables
- Need fewer subjects

- Order effects
- Carry-over effects
- Subjects no longer naïve
- Change may just be a function of time
- Sometimes not logically possible

- Distribution of differences between means
- Heterogeneity of Variance
- Nonnormality

- Effect of fluctuations in DOW: did it effect Pros
- Different question than previously

- Now we have two independent groups of data
- Pros during positive DOW, and Pros during negative DOW
- We want to compare means of two groups

Cannot compute pairwise differences, since we cannot compare two random data points

We want to test differences between the two sample means (not between a sample and population)

- How are sample means distributed if H0 is true?
- Need sampling distribution of differences between means
- Same idea as before, except statistic is (X1 - X2) (mean 1 – mean2)

- Mean of sampling distribution = m1 - m2
- Standard deviation of sampling distribution (standard error of mean differences) =

Cont.

- Distribution approaches normal as n increases.
- Later we will modify this to “pool” variances.

- Same basic formula as before, but with accommodation to 2 groups.
- Note parallels with earlier t

- Each group has 5 data points.
- Each group has n - 1 = 50 - 1 = 8 df
- Total df = n1- 1 + n2 - 1 = n1 + n2 - 2 50 + 50 - 2 = 98 df
- t.01(98) = +2.62 (approx.)

- Two major assumptions
- Both groups are sampled from populations with the same variance
- “homogeneity of variance”

- Both groups are sampled from normal populations
- Assumption of normality
- Frequently violated with little harm.

- Assumption of normality

- Both groups are sampled from populations with the same variance

- Refers to case of unequal population variances.
- We don’t pool the sample variances.
- We adjust df and look t up in tables for adjusted df.
- Minimum df = smaller n - 1.
- Most software calculates optimal df.