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29 Cards in this Set
- Front
- Back
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Types of Experiment Design |
1. Within-Subject 2. Between-Subject 3. Mixed Design |
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Between-Subject Design |
"Makes comparisons b/t groups of subjects to determine effects of I.V."
Two groups get one "variable" each
Advantage: No Carryover effect Disadvantage: Need more participants: individual differences (confounding variable) |
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Within-Subject Design |
"Subjects are exposed to all levels of the manipulation/I.V."
One group gets both variables
Advantage: Need less participants: no individual differences Disadvantage: Carryover effect |
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Mixed Design |
"A combination of the two other designs"
Two groups get the same manipulation
ex. A group of men and a group of women are tested for how alcohol affects driving Hypothesis: alcohol uniformly affects driving, but women are more affected than men. |
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Stroop Effect |
When the color of a word is different than the word itself
(ex. the word "RED" is blue) |
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Definition of a Hypothesis |
A STATEMENT that explains or makes GENERALIZATIONS a set of FACTS OR PRINCIPLES, usually forming a basis for possible experiments to confirm its VIABILITY |
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Selective Attention |
When people focus on a particular object for a certain amount of time, simultaneously ignoring other "irrelevant" information that is also occuring
"Banana in the background" |
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Internal vs. External Validity |
Internal: How much did the I.V. affect the D.V.?
External: Can this data be used to generalize other situations/people?
(External is much more important) |
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What is Statistics? |
The study of the collection, analysis, interpretation, organization, and presentation of data. |
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Population vs. Sample |
A sample is part of a population |
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Population |
Dataset that contains all outcomes, measurements or "responses of interest" |
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Sample |
Dataset that is a subset of the population |
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Parameters vs. Statistics |
Parameters come from the POPULATION
Statistics come from the SAMPLE |
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Qualitative vs. Quantitative |
If you see numbers, it's quantitative. |
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Three ways to measure "central tendency" |
Mean, Median, and Mode |
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Standard Deviation |
the equation above
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Overview of Statistics
Inferential vs. Descriptive |
see the chart below
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Range |
Difference b/t highest and lowest value in a dataset |
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Frequency Distribution |
Organizing data based on how often that data occurs (useful for finding mode) |
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Negatively vs. Positively Skewed |
Negatively: More data towards the right
Positively: More data towards the left |
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Why is Mean worse than Median sometimes? |
Mean is too easily swayed by outliers
If it's skewed, use median |
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Features of normal distribution |
Normal distribution: 1 mode and symmetrical, bell-shaped, mean = median = mode = center of the curve. |
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Normal distribution by percentage |
68% = 1 SD 95.7% = 2 SD 99.7% = 3 SD |
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Null Hypothesis |
The hypothesis if the research hypothesis turns out to be false.
(reject it if the data passes a certain point) |
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Comparison distribution |
The distribution that represents the population situation if the null hypothesis is true.
You need to know mean and SD to explain comparison distribution ("x SD around this mean") |
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How to calculate Z-score |
Z = (cutoffscore-mean)/SD
Z = (X-"mew")/SD |
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When is cutoff score, Z-score, and p-value significant? |
Cutoff: when test score "exceeds" the cutoff score
Z-score: when z-score exceeds cut-off z-score
p-value: when p-value < cutoff p-value |
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Type 1 Error |
Rejecting a null hypothesis when it is true
How to prevent this: tighten the cutoff p-value |
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Type 2 Error |
Failing to reject a false null hypothesis |
