Midterm Flashcards

Question

Sampling Distribution of the Sample Means

Answer 1

A distribution created by the means of many samples

Answer 2

The empirical representation of the sampling distribution of sample means

Answer 3

The standard deviation of the sampling distribution of the sample means

Answer 4

States that the sum/mean of a number of independent variables has approximately a normal distribution, almost whatever the distributions of those variables

Answer 5

Approximately 2; really 1.96, because 95% of the area under the curve is within 1.96 standard deviations to either side of the mean

Answer 6

A probability distribution used when estimating the population mean from a small sample size, when the population SD is unknown. - Similar to normal distribution but with heavier tails = more variability - As sample size increases, t-distribution approaches normal

Answer 7

[M - MoE, M + MoE] - In 95% of cases, this interval will include the unknown population value

Answer 8

The shape of the t-distribution depends on the degrees of freedom (N-1); larger df = closer estimate of population SD - Indicator of how good our estimate is

Answer 9

A statement about the population we want to test (a single value); no change/zero effect

Answer 10

The probability of observing your data, or something more extreme, IF the null hypothesis is TRUE - We assume the null is true when finding p - P less than 0.05 suggests unlikely data (if the null is true), reject the null - P greater than 0.05 means data is consistent with null, fail to reject

Answer 11

The level of significance (usually 0.05) that serves as a criterion to compare p with for rejection - If p is less than the significance level, we reject null (the effect was statistically significant) - If p is more than the SL, we cannot reject null (the effect is statistically insignificant)

Answer 12

Usually 0.05, but researchers often use the smallest value that allows rejection (more convincing) - Lower significance level (0.01 rather than 0.05) gives more evidence AGAINST the null (easier to claim effect)

Answer 13

If the null value falls outside of the confidence interval of sample data (visually), we can reject the null

Answer 14

Reject the null (there was an effect)

Answer 15

Fail to reject the null (no evidence of an effect/null value is true)

Answer 16

Use t instead of z - Use sample SD in place of assumed pop. SD - Same steps but using t distribution instead of z

Answer 17

Null Hypothesis = sample mean is the same as pop mean (50) 1. Find sample mean 2. How much more/less than the null value is it? 3. Find z scores for sample mean (this is where we use the assumed pop. SD); z score tells is how far M deviates from the null value 4. z scores (above and below) determine a CI 5. Add values under tails beyond Z bars on both sides for P value

Answer 18

1. Dichotomous thinking (that an effect is either present or not; better to measure how much or what extent) 2. Statistically significant does not always mean meaningful or large effect, it just means the null is unlikely (it is unlikely that there is NO effect) 3. Not rejecting the null (stating no effect), does not mean that this is true is reality 4. P is not the odds THAT the null is true, it is the odds of obtaining OUR result IF the null is true (there actually was no effect) 5. P varies greatly, the CI is more trustworthy

Answer 19

Significance level; reject null if p < ɑ, cannot reject if p > ɑ - Type I error rate (probability of rejecting null when its true) - Assumes null is true

Answer 20

Type II: there is an effect, but we missed it (false negative)

Answer 21

Type I: there was no effect, but we thought there was (false positive)

Answer 22

Alpha: probability of rejecting null when it was actually true (probability of making a type I error)

Answer 23

Beta: probability of accepting null, when it is actually false (probability of making a type II error) - Assumes alternative hyp. is true - Rate of false negatives

Answer 24

our chance of correctly rejecting the null hypothesis when it is in fact false - Sensitivity; probability of finding an effect that is in fact there - Influenced by N, effect size (larger is easier to see), and alpha (sig. level)

Answer 25

Each participant is only tested on one of two conditions being compared; two conditions are separate from each other - Between-Subjects - Null = condition 1 = condition 2 in the population & in the sample

Answer 26

Mdiff = (M2 - M1)

Answer 27

Marks the difference with a solid triangle lined up with M2

Answer 28

1. Random Sampling 2. Normally distributed population 3. Homeogeneity of variance (SD of both groups is assumed to be the same)

Answer 29

1. T component (df) 2. Variability component (pooled SD; Sp) 3. Sample size component - Gives us MoE for the difference CI = [diff. of Means - MoE , diff. of Means + MoE]

Answer 30

Always longer due to variability in differences between means

Answer 31

A measure of effect size that shows the standardized difference between two mean groups, in units of SD - d = effect size in original units / an appropriate SD (standardizer); this is the same units as d - Essentially tells us how many SDs the two groups are apart

Answer 32

- Estimated population SD - Pooled SD (better) - Preferred to have the same standardizer for both independent groups to allow for comparision

Answer 33

Small effect = ~0.2 Medium effect = ~0.5 Large effect = ~0.8+

Answer 34

Delta (lowercase) meaning the difference between the groups in the population

Answer 35

If two CIs just touch, p = ~.01 (moderate difference) If two CIs overlap moderately, p = ~.05 (small difference)

Answer 36

A single group of participants experience both IV conditions; each participant provides 2 data sets - Variability component is the SD of the differences between paried scores 1. Difference for each pair 2. Mean of differences 3. SD of the differences - Mdiff = new-old

Answer 37

Smaller; more precise effect size measure - This is due to smaller SD as effect is within-subject, not between-subject

Answer 38

Correlation; measures the strength of association ~1 = positive correlation 0 = no correlation ~ -1 = negative correlation

Answer 39

The standardizer is the standard deviation of the differences (for a single pair rather than two different groups) d = Mdiff / Sav

Answer 40

Finds P to allow for NHST; can we reject the null? 1) Single group: t(df) = Effect Size / S x (1/sqrtN) 2) Paired Design: t(N-1) = Mdiff / Sdiff x (1/ sqrtN)

Answer 41

any influence of one measure on another

Answer 42

Counterbalancing: assignment of different participants to different orders of presentation or different versions of the same condition

Answer 43

Versions of a test that use different questions, but measure the same characteristic & are similar in difficulty

Answer 44

An unwanted difference between groups that limits the conclusions drawn, or an unwanted influence on effect size being estimated in repeated measure designs - Effects both IV and DV creating a misleading association between them - EX., studying exercise and weight loss, but don't control for the confounding variable of diet, leading you to think exercise had the only effect on outcomes

Answer 45

Has a single IV with more than two levels - One-way refers to only one IV - Independent groups means there is no overlap between groups - Ex., effect of diet (Diet A, Diet B, and Diet C) on weight loss

Answer 46

Select a few comparisons that correspond with the reasearch questions; use CIs to guide interpretations - Uses a One-Way ANOVA: compares varience between groups to the varience within the groups - results in an F-ratio - Run post-hoc tests to see which diets lead to different outcomes

Answer 47

the difference between means of two subsets of group means (instead of comparing all groups at once); focuses on smaller parts of the data

Midterm Flashcards

(72 cards)