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PSYC 260 > Test 3 > Flashcards

Test 3 Flashcards

1
Q

Null Hypothesis (Ho)

A

no true effect on pop; the apparent effect could be a matter of sampling error

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2
Q

Alternative Hypothesis (H1)

A

true effect in population (often called research hypothesis)

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3
Q

What do we estimate with hypothesis testing?

A

We estimate the probability that the null hypothesis is true

  • If prob is very low (.05 or less) then reject Ho and decide that the apparent effect is probably a true effect
  • If prob is greater than .05, then fail to reject Ho and conclude that any apparent effect may be a matter of sampling error
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4
Q

Z formula for hypothesis testing

A

z = m - μ / σm

remember that σm = σ / square root of N

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5
Q

Nondirectional (two-tailed) test

A

The alternative hypothesis is that the treatment has an effect, without specifying the direction:
H1: μT ≠ μu
ALWAYS use this in class

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6
Q

Directional (one-tailed)

A

If there is an empirical and or theoretical basis for believing that the effect can only occur in one direction, then a directional test may be used:
H1: μT > μu
or H1: μT < μu

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7
Q

Significance level α

A

α = probability of rejecting the null hypothesis when it is in fact true
Set at .05 or .01
Try to minimize claims that a treatment has an effect when it does not

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8
Q

Type I Error

A

When you reject null but the null is true

p = α

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9
Q

Type II Error

A

When you fail to reject null but the null is false

p = β

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10
Q

Correct decision p = 1 - β

A

when you reject null when null is false

- power

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11
Q

Correct decision p = 1 - α

A

when you fail to reject null when null is true

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12
Q

Power

A

probability of rejecting the null hypothesis when it is in fact false (p= 1 - β)

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13
Q

How do you increase power?

A

by:

  • Increasing sample size
  • Increasing alpha, but cannot go higher than .05
  • Using a one-tailed test (if the effect is in the predicted direction)
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14
Q

Underlying assumptions (hypothesis testing)

A
  • At least an interval level of measurement
  • Random sample
  • The sampling dist. of the mean is normally distributed, which is likely when:
  • —-The sample is selected from a population that is normally distributed
  • —-N is very large (central limit theorem)
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15
Q

Robustness

A

probability of a type I error (rejecting Ho when it is true) is close to α even when the assumptions underlying the use of an inferential statistic are violated

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16
Q

Cohen’s d for hypothesis testing

A 
effect size
d = m - μ / σ
small: d < .2
med: d is .2 - .8
large: d > .8
17
Q

t statistic

A

t = m - μ / sm

18
Q

What is the difference between t and z distributions?

A

t distributions change with the sample size (or technically the df, which is n-1)
- as sample size (df) decreases, t becomes flatter and more heavy (higher probabilities) in the tails

19
Q

When a re T-tests are most commonly used to make statistical inferences about the effect of an IV on a DV?

A 
when:
The IV has only 2 levels
Often experimental conditions
- Treatment and control
The DV is measured on an interval scale
20
Q

Types of Two-Independent Samples

A

Between-subjects (independent groups)

Within-subjects (related samples, repeated measures)

21
Q

Between-subjects (independent groups)

A

Comparisons are made between separate (independent) groups (samples) of participants
Each group is assigned to a different level of the IV
To get comparable groups, use RA

22
Q

Within-subjects (related samples, repeated measures)

A

Participants are either not separated into independent groups (repeated measures) or are related to one another
Most often, each individual participates at all levels of the IV- thus, the same individuals are compared over different levels of the IV

23
Q

Two kinds of Two-sample t-tests

A

Independent Samples

Related samples

24
Q

Independent Samples

A

Use with independent-groups designs

Compare separate groups of individuals

25
Q

Related samples

A

Use primarily with within-subjects designs
Compare between experimental conditions, not between groups
Non-independent- either:
-Repeated measures: same individuals measured more than once
-Matched pairs: matched on common characteristics

26
Q

Conditions for independent samples

A

Comparing 2 independent groups (samples)

Both groups measured on the same interval measure

27
Q

Assumptions for independent samples

A
  • Random selection
  • Homogeneity of pop. variances
  • The sampling distribution for the difference between the means is normally distributed- will be true when:
  • —-The population distributions for both conditions are normal
  • —–n is large (>30)
28
Q

df for two-independent sample t tests

A

df = n1 + n2 - 2

29
Q

Conditions for related samples

A

Comparing the same (or related) participants in 2 conditions

Interval level

30
Q

Assumptions for related samples

A
  • Random selection
  • The sampling distribution for the difference between means is normally distributed- true when:
  • —The populations for both conditions are normally distributed
  • —n is large
31
Q

df for related samples t tests

A

df = nD - 1

32
Q

Why is there reduced error variance with a within-subjects design?

A

Rationale for using difference scores:

  • Between conditions overall individual differences on the DV are controlled because the same (or related) individuals are in both conditions
  • Each participant serves as their own control
  • By using difference scores we:
  • –Take away overall individual differences on the DV
  • –Yet maintain the apparent size of the effect on the IV for each participant
33
Q

Goal for confidence interval estimation

A

be 95% confident that our interval contains μ

34
Q

Why use standard normal distribution as a model for sampling distribution?

A
  • Areas (proportions of scores) under it are known
  • Good fit – the sampling distribution is likely to be normal in form if either one of the following holds:
  • —the scores are randomly selected from a population that is normally distributed
  • —n is sufficiently large (Central Limit Theorem)
35
Q

Why use a t distribution as a model for the sampling distribution?

A
  • Areas under them are known
  • Good fit – t distributions have symmetrical bell-shapes
  • When σ is unknown a t-distribution provides a more accurate model than does the standard normal distribution
36
Q

2 goals for estimating in inferential statistics

A

Two goals:

  1. Make unbiased estimates
  2. Assess error made when making estimates
    - A sampling distribution helps accomplish these goals
37
Q

Sampling distributions and why its useful

A

A distribution of a statistic derived from all possible samples of a given size

Useful for:

  • establishing the rationale for estimation of population parameters from statistics
  • assessing the amount of error we are likely to make when using a statistic to estimate a parameter
38
Q

When to use z-test

A

when we know population SD

39
Q

When to use t-test

A

when we do not know population SD

PSYC 260 (6 decks)

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