Inferential Statistics Flashcards
1
Q
Inferential Statisitcs
A
Techniques that allow us to study samples and then make generalizations about the populations from which they are selected.
2
Q
Chance
A
Could affect results when inferences about populations are made from samples
3
Q
Hypothesis Testing
A
Statistical method that uses sample data to evaluate a research hypothesis about a population parameter
4
Q
Hypothesis-Driven Research Steps
A
- State a research hypothesis about a population
- Set criteria for a description
- Obtain a random sample from a population and compute sample statistics
- Make a decision (accept/reject null hypothesis)
5
Q
Null Hypothesis
A
- No change, effect, difference, or relationship
- Cannot reject if weak evidence or insufficient power
6
Q
Alternative Hypothesis
A
- Change, effect, difference, relationship from the general population
- Non-directional: doesn’t specify direction of effect, more common and conservative
- Directional/one-tailed: direction of association, rarer
7
Q
Non-Directional
A
- Two-tailed test
- More common, conservative, and convential
- No need to “guess” direction of association
- Even if association occurred in direction opposite from expected, it will be tested
8
Q
Setting Criteria
A
- Define level of significance (alpha) for hypothesis test
- Probability of erroneously rejecting Ho when it is true
- Usually set to 0.05 (5%)
9
Q
Collecting Data/Computing Statistics
A
- Check assumptions (random, independent, observations, homogeneity of variance, normality)
- Decide whether parametric or non-parametric test should be used
- Compute the appropriate test statistics: Z-score, T-score, Chi-square, r statistic
10
Q
Decision Making
A
- Reject Null: there is an association between independent and dependent variables
- Failure to reject null: appears to have no effect
11
Q
P-Value
A
- Probability of result occuring by chance
- Smaller = less likely to be due to chance
- If p < alpha = reject Ho
- Since alpha usually set to 0.05, p < 0.05 to have statistically significant results
12
Q
Alpha Level
A
- Level of statistical significance (max probability of making a Type I error)
- Test statistic compared to predefined “significance” level
- Allow 5% chance usually
- Arbitrary, but customary
- Can be 0.01 or 0.1 too (more/less conservative respectively
- Low alpha can be chosen in some situations (EX: multiple comparisons)
13
Q
Statistically Significant
A
Happens when….
- Null hypothesis is rejected
- Result is unlikely due to chance
- *Gives no information about magnitude of association or clinical significance**
14
Q
P-Value Influencers
A
- Magnitude of association (how big of a difference)
- Sample size
- Variation in observed outcome
No p-value excludes or mandates chance
15
Q
P-Value Misconceptions
A
- Calculates probability, not a clear yes or no
- 0.05 is ARBITRARY
- Does not imply causality
- Statistically significant is NOT the same as clinically significant
16
Q
Type of Errors
A
- Type 1 Error (alpha): rejecting Ho when it’s true, significance level
- Type 2 Error (beta): failure to reject a false Ho
- Power: probability of rejecting Ho when it is false
17
Q
Factors Impacting Power
A
- Sample size: power increases with its increase
- Level of significance (alpha): power decreases as it decreases
- Beta (type II error)
- Choice of statistical test used
- Variability (precision) of outcome variable: power increases as it decreases
- Effect size: increases power as it increases (large difference between groups)
18
Q
Confidence Interval
A
- CI: range of values likely to cover true parameter
- Built around point estimate
- Point estimates +/- margin or error
- 90%, 95%, 99% usually (arbitrary), 95% most common since alpha is usually 5%
19
Q
95% CI
A
- Addresses precision of point estimates: range of values that lies within 95% confidence
- Can be used for hypothesis testing
- Can indicate is results are statistically significant
20
Q
Difference in Means
A
- Provides index of variability in group mean differences that would be expected by chance
- Difference between means = 0, no association (Ho = true)
- If “0” isn’t included within the interval, we can conclude that the means are different
21
Q
Width of CI
A
- Indication of precision
- Wider the interval, the less precise
22
Q
Precision Affectors
A
- Level of confidence - larger level of confidence makes the CI larger
- Sample size: larger n causes smaller CI (more precise)
MORE CONFIDENT = LESS PRECISE
