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Questions and Answers
What were the two main factors that the experimental units were exposed to in this study?
What were the two main factors that the experimental units were exposed to in this study?
The two main factors were daily caloric intake and daily exercise level.
List the levels of daily caloric intake set up in the experiment.
List the levels of daily caloric intake set up in the experiment.
The levels were 1500 – 2000 calories and 2000 – 2500 calories.
How many treatment groups were formed in the experiment, and what was the sample size for each group?
How many treatment groups were formed in the experiment, and what was the sample size for each group?
There were 6 treatment groups, with a sample size of 80 men in each group.
What potential bias was identified in this experiment regarding exercise levels?
What potential bias was identified in this experiment regarding exercise levels?
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Explain the significance of random assignment in this study.
Explain the significance of random assignment in this study.
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What is the primary difference between a sample and a population?
What is the primary difference between a sample and a population?
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What is the characteristic of Simple Random Sampling?
What is the characteristic of Simple Random Sampling?
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Explain the two main branches of statistics.
Explain the two main branches of statistics.
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How does Stratified Random Sampling differ from Simple Random Sampling?
How does Stratified Random Sampling differ from Simple Random Sampling?
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What is the process of Cluster Sampling?
What is the process of Cluster Sampling?
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Define univariate data and provide an example.
Define univariate data and provide an example.
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What distinguishes qualitative variables from quantitative variables?
What distinguishes qualitative variables from quantitative variables?
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Describe Systematic Sampling and its selection method.
Describe Systematic Sampling and its selection method.
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What is Convenience Sampling and where is it commonly applied?
What is Convenience Sampling and where is it commonly applied?
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Describe discrete quantitative variables and give an example.
Describe discrete quantitative variables and give an example.
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Explain Judgement Sampling and its purpose.
Explain Judgement Sampling and its purpose.
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What is the purpose of a sampling design in research?
What is the purpose of a sampling design in research?
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Differentiate cross-sectional data from time-series data.
Differentiate cross-sectional data from time-series data.
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What distinguishes Observational Studies from Designed Experiments?
What distinguishes Observational Studies from Designed Experiments?
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What is the difference between a census and a sampling survey?
What is the difference between a census and a sampling survey?
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Why is randomness important in sampling designs?
Why is randomness important in sampling designs?
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What is the purpose of a control group in an experiment?
What is the purpose of a control group in an experiment?
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Define randomization in the context of experimental design.
Define randomization in the context of experimental design.
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What are the three key principles of experimental design?
What are the three key principles of experimental design?
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Explain what a completely randomized design involves.
Explain what a completely randomized design involves.
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What distinguishes a double-blind experiment from a blind experiment?
What distinguishes a double-blind experiment from a blind experiment?
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How does replication contribute to an experimental study?
How does replication contribute to an experimental study?
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What role do levels play in experimental factors?
What role do levels play in experimental factors?
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Describe the importance of sampling from similar backgrounds in an experimental study.
Describe the importance of sampling from similar backgrounds in an experimental study.
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Study Notes
Basic Statistics Definitions
- Population: The entire group being studied. Represented by N, the size of the population.
- Sample: A subset of the population, used for analysis and projection back to the population. Represented by n, where n ≤ N.
- Descriptive Statistics: Mathematical methods for organizing and summarizing information.
- Inferential Statistics: Methods to draw conclusions about a population based on a sample, assessing the reliability of those conclusions.
Key Concepts
- Element/Experimental Unit: A single member of a population or sample.
- Sampling: The process of selecting elements from a population to create a sample, often with a specific methodology (Sampling Design).
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Variable: A characteristic of the population members that can be measured.
- Qualitative Variable: Characterizes non-numerical features.
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Quantitative Variable: Measures characteristics using numerical scales.
- Discrete Quantitative Variable: Can only take specific values, usually integers.
- Continuous Quantitative Variable: Can take on any value within a given range.
Sampling Designs
- Simple Random Sampling: Every population member has an equal chance of selection. Uses random number generators/tables.
- Stratified Random Sampling: Random selection from subgroups (strata) within the population, with the proportion of elements from each stratum proportional to their representation in the whole population. Can choose specific numbers from each stratum to represent demographics (quota sampling).
- Cluster Sampling: Dividing the population into naturally occurring clusters and then randomly choosing clusters (one-stage) or selecting sub-samples from chosen cluster (two-stage).
- Systematic Sampling (1-in-m sampling): Choosing a random starting point and selecting every mth element thereafter.
Data Types
- Univariate Data: Data describing a single characteristic of items.
- Bivariate Data: Data about two characteristics.
- Multivariate Data: Data involving three or more characteristics.
- Cross-Sectional Data: Data collected at a single point in time across a population.
- Time Series Data: Data collected at multiple points in time for a population.
- Census: Data collected from all population members.
- Sampling Survey: Data collected from a sample subset of a population.
Experimental Designs
- Observational Studies: Researchers observe and measure characteristics in a sample.
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Designed Experiments: Researchers apply treatments and controls to observe the effect on a variable
- Treatment: An experimental condition
- Response Variable: A variable measuring the experimental effect
- Factor: A variable of interest influencing the effect
- Levels: Possible values of a factor
- Treatment Group: A group experiencing a treatment (or more).
- Control Group: A similar group with no treatment or baseline treatment.
Principles of Experimental Design
- Control: Reduces effects of confounding factors (not the one of interest)
- Randomization: Randomly assigns elements to groups to minimize bias.
- Replication: Uses a sufficient number of participants for the randomization to produce similar groups.
Specific Design Examples
- Completely Randomized Design: All experimental units are randomly assigned to treatments.
- Randomized Block Design: Experimental units are assigned randomly to treatments in blocks (e.g., by similar characteristics).
- Blind Experiment: Participants are unaware of their assigned treatment (minimizes bias).
- Double-Blind Experiment: Neither participants nor experimenters know assignments (minimizing bias).
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Description
Test your knowledge of key statistical terms and concepts. This quiz covers definitions such as population, sample, descriptive and inferential statistics, and more. Perfect for students looking to strengthen their understanding of basic statistics.