Null Hypothesis Significance Testing Flashcards
Why does sample size matter?
The larger our sample size, the closer our sample resembles the distribution
Define replication
Attempt to reproduce a finding from another study in a new sample using identical methodology
What is the focus of confirmatory research?
Testing specific hypotheses
“Null Hypothesis Significance Testing” (NHST)
What is exploratory research?
Doesn’t start with a hypothesis
Can collect new data or work with existing data
Useful for generating hypotheses, but statistical tests can be difficult to interpret
What are three qualities of a good research question?
- Researchable and realistic
- Informed by prior research
- Not too broad and not too narrow - proportional to the project at hand
Define hypothesis
A testable prediction - a statement about what we reasonably believe our data will show
What are three different levels a hypothesis can be defined on?
Conceptual, operational, statistical
What is a conceptual hypothesis?
Describes our prediction in conceptual terms. Can be defined in terms of the direction of the effect that we’re studying
What is meant by operationalisation?
The process of translating concepts into measures
What does a statistical hypothesis predict?
What do we expect to happen numerically?
How are the null and alternative hypotheses denoted?
N = H0 (small 0)
A = H1 (small 1)
When using Null Hypothesis Significance Testing, what are we trying to decide?
Whether we can reject the null hypothesis
What is meant by the 𝛼 level?
What should the decision be based on?
The rate of false-positive findings we’re willing to accept if we’re living in a reality where the null hypothesis is true (If we were to repeat our experiment over and over again, how often are we willing to incorrectly reject the null hypothesis?)
The decision should be based on a cost benefit analysis
What do psychologists often use as the 𝛼 rate?
𝛼 rate of 5%
When p is what value, we reject the null hypothesis
0.05/5%
What exactly does the p value tell us?
The probability of observing a test statistic at least as large as the one we observed in our sample if the null hypothesis is true
(How likely the detected effect is IF the null is true)
Define the following terms:
1. Effect sizes
2. Confidence intervals
3. Power analysis
- How large/meaningful is the effect (e.g.mean difference) that we found
- what are the plausible limits of our effect?
- determining the necessary sample size before we begin data collection
What is statistical power?
The probability of detecting an effect of a certain size as statistically significant, assuming the effect exists
Finish the sentences about power analysis:
1. Makes non-_____ effects easier to interpret
2. Reduces over-______ and wasting resources
3. Reduces the ________ of missing an important finding
4. A statistical power of __% is considered the standard to aim for
- significant
- sampling
- probability
- 80
