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10 Cards in this Set

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t-statistic
- requires sample Mean = M
- requires sample variance s²

- unknown population otherwise we would use z-score
= do not have μ (population mean) or
t-statistic
Procedure
step 1 : State Hypothesis and alpha (α) level ( .05, .01)
step 2 : Locate critical region
step 3 : Calculate
step 4 : Make a decision

Calculate Effect Size
H₀: u = y
H₁: u ≠ y
α = 0.05

critical region:
df = n-1, look up t-table for either 1 or 2 tailed test with α level

calculate:
s² = SS / n-1(df)

Sm = √ s² / n
OR
Sm = s / √n

Degrees of Freedom
df = n-1

- determines how well the distribution of t approximates a normal distribution
=> for large values of df the t distribution will appear normal
=> with small values of df the distribution will appear flatter and spread out than normal distribution

- describes how well the t statistic represents a z-score
Cohen's d
Measure of effect size:
small, medium, large

estimated Cohen's d => mean difference / standard deviation
M - μ / s
Effect size:
percentage of variance acct'd for => r² = t² / t² + df * 100
Estimated standard error (Sm)
- used when
Directional hypothesis
- One tailed test

H₀: μ ≥ y
H₁: μ < y

Or

H₀: μ ≤ y
H₁: μ > y
Sample variance
s² = SS / n-1

OR

s² = SS / df
sample standard deviation
s = √ SS / n-1

OR

√ SS / df
Independent measures design
- a research design that determines if there are differences between two separate samples (two groups)

- uses a separate group participants for each tx condition (or for each population) AKA Between-subjects design