Podcast
Questions and Answers
In an experiment examining the impact of sleep duration on test performance, what represents the independent variable (IV)?
In an experiment examining the impact of sleep duration on test performance, what represents the independent variable (IV)?
- The duration of sleep the night before the test. (correct)
- The students' anxiety levels.
- The score achieved on the test.
- The difficulty of the test questions.
A negative correlation between exercise and weight indicates that as exercise increases, weight tends to increase as well.
A negative correlation between exercise and weight indicates that as exercise increases, weight tends to increase as well.
False (B)
What does a high standard deviation indicate about a dataset?
What does a high standard deviation indicate about a dataset?
Greater variability
A researcher rejects the null hypothesis when it is actually true. This is an example of a Type ______ error.
A researcher rejects the null hypothesis when it is actually true. This is an example of a Type ______ error.
Match the following terms with their definitions:
Match the following terms with their definitions:
If a depression scale consistently produces similar results over multiple administrations but does not accurately capture the construct of depression, it is said to have:
If a depression scale consistently produces similar results over multiple administrations but does not accurately capture the construct of depression, it is said to have:
Cohen's d measures statistical significance, indicating whether an effect is likely due to chance.
Cohen's d measures statistical significance, indicating whether an effect is likely due to chance.
What is the primary purpose of using a Z-score?
What is the primary purpose of using a Z-score?
The ______ hypothesis states that there is no effect or difference between the groups being studied.
The ______ hypothesis states that there is no effect or difference between the groups being studied.
In hypothesis testing, what does a larger sample size generally lead to?
In hypothesis testing, what does a larger sample size generally lead to?
Flashcards
Independent Variable (IV)
Independent Variable (IV)
The variable you manipulate in an experiment.
Dependent Variable (DV)
Dependent Variable (DV)
The outcome you measure in an experiment.
Categorical Variables
Categorical Variables
Variables with distinct categories.
Continuous Variables
Continuous Variables
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Mean
Mean
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Median
Median
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Mode
Mode
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Standard Deviation
Standard Deviation
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Correlation
Correlation
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Null Hypothesis (H0)
Null Hypothesis (H0)
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Study Notes
- Study notes below
Variables
- An independent variable (IV) is manipulated in an experiment
- A dependent variable (DV) is the outcome that is measured
- Categorical variables have categories
- Continuous variables can take any value within a range
Central Tendency & Variability
- Mean is the average score
- Median is the middle score when data is ordered
- Mode is the most frequent score
- Standard deviation indicates how spread out the data is around the mean
- Variance is the square of the standard deviation
Correlation & Covariance
- Correlation shows how two variables are related
- Positive correlation: As one variable goes up, so does the other
- Negative correlation: As one variable goes up, the other goes down
- Covariance is a measure of how two variables vary together
Reliability & Validity
- Validity measures how well an experiment measures what it claims to
- Content, construct, and criterion are types of validity
- Reliability measures the consistency of the measurement
Hypothesis Testing
- Null hypothesis (H0): There’s no effect or difference
- Alternative hypothesis (H1): There is an effect or difference
- Type I error involves a false positive, rejecting a true null hypothesis
- Type II error involves a false negative, failing to reject a false null hypothesis
- Larger sample sizes increase statistical power, so it's easier to detect a true effect
Z-scores & Z-tests
- Z-score indicates how many standard deviations a score is from the mean
- Z-test is used when the population variance is known, to compare a sample mean to a population mean
Statistical Significance & Effect Sizes
- Statistical significance indicates whether an effect is likely due to chance (p-value)
- Effect Size (Cohen’s d) measures the size of the effect, independent of sample size
- Small effect = 0.2
- Medium = 0.5
- Large = 0.8
- Confidence intervals show the range of values that are likely to contain the population parameter
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