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Questions and Answers
A cognitive neuroscientist aims to investigate the neural correlates of decision-making under risk using fMRI. Participants are recruited from a local university subject pool. Considering the principles of statistical inference, which of the following statements best describes the limitations concerning generalizability?
A cognitive neuroscientist aims to investigate the neural correlates of decision-making under risk using fMRI. Participants are recruited from a local university subject pool. Considering the principles of statistical inference, which of the following statements best describes the limitations concerning generalizability?
- The absence of a true zero point in the BOLD signal measurements introduces systematic error, rendering ratio comparisons across individuals and conditions statistically meaningless, thus limiting generalizability.
- The relatively small sample size typically employed in fMRI studies significantly reduces statistical power, limiting the ability to detect meaningful effects and generalize findings to the broader population.
- The use of fMRI inherently limits the ecological validity of the study, making it difficult to generalize findings to real-world decision-making scenarios.
- The reliance on a convenience sample from a university population restricts the external validity of the study, potentially biasing results due to demographic and cognitive homogeneity. (correct)
In experimental design, prioritizing a large sample size invariably outweighs the importance of ensuring that the sample is truly representative of the target population, especially when employing advanced statistical techniques to control for confounding variables.
In experimental design, prioritizing a large sample size invariably outweighs the importance of ensuring that the sample is truly representative of the target population, especially when employing advanced statistical techniques to control for confounding variables.
False (B)
A researcher is studying the effects of a novel drug on reaction time. Identify the level of measurement for reaction time, measured in milliseconds, and explain why it is classified as such.
A researcher is studying the effects of a novel drug on reaction time. Identify the level of measurement for reaction time, measured in milliseconds, and explain why it is classified as such.
Ratio. Reaction time is a ratio scale because it has a true zero point (representing no time elapsed) and allows for meaningful ratio comparisons (e.g., one reaction time can be twice as fast as another).
In statistical inference, a(n) ______ is a value that describes a characteristic of a population, while a(n) ______ is a value computed from a sample used to estimate the corresponding population value.
In statistical inference, a(n) ______ is a value that describes a characteristic of a population, while a(n) ______ is a value computed from a sample used to estimate the corresponding population value.
Match the following levels of measurement with the appropriate statistical analysis that can be applied:
Match the following levels of measurement with the appropriate statistical analysis that can be applied:
An epidemiologist is investigating the prevalence of a rare genetic disorder in a geographically isolated population. Genetic testing is conducted on a sample of individuals, and disease status is recorded as either 'affected' or 'unaffected.' Considering the nature of this data, which level of measurement is most appropriate?
An epidemiologist is investigating the prevalence of a rare genetic disorder in a geographically isolated population. Genetic testing is conducted on a sample of individuals, and disease status is recorded as either 'affected' or 'unaffected.' Considering the nature of this data, which level of measurement is most appropriate?
A researcher commits the ecological fallacy when inferring individual-level conclusions solely from aggregate data, but this fallacy can be mitigated by employing sufficiently complex hierarchical modeling techniques that account for individual variability within group-level effects.
A researcher commits the ecological fallacy when inferring individual-level conclusions solely from aggregate data, but this fallacy can be mitigated by employing sufficiently complex hierarchical modeling techniques that account for individual variability within group-level effects.
In a study examining the effects of sleep deprivation on cognitive performance, participants are assigned to either a sleep-deprived group or a control group. Cognitive performance is assessed using a battery of neuropsychological tests, producing scores that are assumed to be normally distributed. Considering the levels of measurement, what is the most accurate classification of the group assignment variable itself?
In a study examining the effects of sleep deprivation on cognitive performance, participants are assigned to either a sleep-deprived group or a control group. Cognitive performance is assessed using a battery of neuropsychological tests, producing scores that are assumed to be normally distributed. Considering the levels of measurement, what is the most accurate classification of the group assignment variable itself?
Flashcards
Population
Population
The complete set of all individuals of interest in a study.
Sample
Sample
A subset of individuals selected from a population, meant to represent it.
Parameter
Parameter
A summary measure that describes a characteristic of a population.
Statistic
Statistic
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Qualitative Data
Qualitative Data
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Quantitative Data
Quantitative Data
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Nominal Level
Nominal Level
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Ordinal Level
Ordinal Level
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Study Notes
Levels of Measurement & Frequency Distributions
- Population: All individuals of interest in a study, described using parameters (e.g., frequency counts, means, standard deviations).
- Sample: Individuals selected from the population, usually intended to represent the population, often a convenience sample for lab experiments (not random). Sample characteristics are described using statistics, e.g., correlations, t-tests.
Review of Variables
- Qualitative variables: Descriptive, based on language.
- Quantitative variables: Numerical, either:
- Categorical variables: Not numerical, based on qualitative properties (e.g., color, breed, gender).
- Numeric variables: Represent measurable quantities as numbers (e.g., "how many", "how much").
- Discrete variables: Finite number of possible values (e.g., number of naps).
- Continuous variables: Infinite number of possible values (e.g., page counts).
Reducing Error in Inferences
- Representative samples: Aim for samples similar to the broader population, though often challenging in experiments. Convenience samples may be necessary.
- Sample size: Larger samples are better, but representativeness is crucial. A small, representative sample is better than a large, unrepresentative one.
Four Levels of Measurement
- Nominal: Categories (e.g., yes/no, categories of objects), numbers are arbitrary labels.
- Ordinal: Categories ranked in terms of size or magnitude, but distance between ranks may not be equal.
- Interval: Ordinal properties plus equal intervals between categories, but no true zero point (absence of a quantity). Ratios are not meaningful.
- Ratio: Interval properties plus true zero point, ratios are meaningful.
Levels of Measurement Summary Table
| Level of Measurement | Equal Intervals? | True Zero Point? | Type of Data |
|---|---|---|---|
| Nominal | No | No | Qualitative |
| Ordinal | No | No | Quantitative |
| Interval | Yes | No | Quantitative |
| Ratio | Yes | Yes | Quantitative |
Kurtosis and Skewness
- Kurtosis: Describes the shape of a distribution (e.g., platykurtic, mesokurtic, leptokurtic).
- Skewness: A measure of asymmetry of a distribution (e.g., negatively skewed, positively skewed, symmetrical). Histograms can reveal skewness.
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Description
Brief overview of populations, samples, and types of variables. Includes qualitative, quantitative, categorical, numeric, discrete, and continuous variables. Error reduction focuses on understanding these variable types and distributions.
