Podcast
Questions and Answers
What are the two main types of statistical tests in hypothesis testing?
What are the two main types of statistical tests in hypothesis testing?
What is a common type of parametric test that analyzes cause-and-effect relationships?
What is a common type of parametric test that analyzes cause-and-effect relationships?
Regression Tests
What type of data is divided into categories or groups?
What type of data is divided into categories or groups?
Categorical Data
Positive skew indicates that data points are spread evenly.
Positive skew indicates that data points are spread evenly.
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What is the term for data points connected from the same source?
What is the term for data points connected from the same source?
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Which of the following best describes Descriptive Statistics?
Which of the following best describes Descriptive Statistics?
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Effective data management includes ethical collection procedures, handling missing data, and ensuring _____ integrity.
Effective data management includes ethical collection procedures, handling missing data, and ensuring _____ integrity.
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Name one ethical practice in data collection.
Name one ethical practice in data collection.
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What is one of the key reasons why data collection methods are essential in research?
What is one of the key reasons why data collection methods are essential in research?
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What are the two main types of statistical tests?
What are the two main types of statistical tests?
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Which statistical test is used to analyze cause-and-effect relationships?
Which statistical test is used to analyze cause-and-effect relationships?
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What type of data is classified into categories or groups?
What type of data is classified into categories or groups?
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Match the following types of data with their descriptions:
Match the following types of data with their descriptions:
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What is a characteristic of a normal distribution?
What is a characteristic of a normal distribution?
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Continuous data can have a finite number of values.
Continuous data can have a finite number of values.
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What should ethical data management practices include?
What should ethical data management practices include?
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The statistical treatment of data contributes to the credibility of findings and supports __________.
The statistical treatment of data contributes to the credibility of findings and supports __________.
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What is the role of descriptive statistics?
What is the role of descriptive statistics?
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Which of the following is an ethical consideration in data collection?
Which of the following is an ethical consideration in data collection?
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Study Notes
Statistical Tests
- Statistical tests are used to determine if a predictor variable has a statistically significant relationship with an outcome variable
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Parametric Tests assume certain properties of the data
- Regression Tests: analyze cause-and-effect relationships
- Comparison Tests: assess differences among group means
- Correlation Tests: examine relationships without hypothesizing causation.
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Non-Parametric Tests do not assume specific characteristics about the data
- Kruskal-Wallis Test: Compares three or more groups
- Mann-Whitney U Test: Compares two independent groups
- Wilcoxon Signed-Rank Test: Tests differences between two related groups
- Chi-Square Test: Compares observed and expected results
Data Types
- Categorical Data is classified into categories or groups (e.g., gender, color)
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Numerical Data is counted or measured and can be further classified into:
- Discrete Data: Counted values that cannot be divided
- Continuous Data: Measured values with an infinite number of possible values
Data Distribution
- Normal Distribution has most data points clustered around a central value, forming a bell-shaped curve (e.g., human height)
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Skewed Distribution is when data is not evenly spread
- Positive Skew (right-skewed) has low values with a few high values
- Negative Skew (left-skewed) has high values with a few low values
Nature of Observations
- Paired Observations are data points connected from the same source (e.g., pre- and post-treatment measurements)
- Unpaired Observations are independent data points from different groups (e.g., test scores from different schools)
Statistical Techniques
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Descriptive Statistics summarizes and characterizes data without generalizing to a population
- Key metrics include averages, ranges, and standard deviations, often presented graphically
- Inferential Statistics extends inferences from sample data to the overall population, allowing researchers to make predictions and generalizations
Data Management
- Ethical collection procedures: obtaining informed consent, anonymization, and secure data storage
- Handling missing data
- Ensuring data integrity through cleaning and screening
Data Presentation
- Visuals such as tables, graphs, and charts enhance clarity
- Clear labeling, logical organization, and the inclusion of relevant statistical notations
Importance of Data Collection Methods
- Quality and Accuracy: Well-formulated methods ensure that the data is accurate and relevant for the research
- Significance, Credibility and Consistency: ensures that the information gathered is aligned with research objectives, accurately measures its intended outcomes, and is consistently reproducible over time.
- Elimination of bias and Generalizability: deliberately chosen techniques can help lessen biases and ensure a representative sample that strengthens the findings’ applicability
- Data-driven Decision Making: Consistent and accurate data create a strong basis for making well-informed decisions influenced by research findings.
Statistical Tests in Hypothesis Testing
-
Parametric Tests: Assume data properties
- Regression Tests: Analyze cause-and-effect
- Comparison Tests: Assess group mean differences
- Correlation Tests: Examine relationships without causation
-
Non-Parametric Tests: Don't assume specific data characteristics
- Kruskal-Wallis Test: Compares 3+ groups
- Mann-Whitney U Test: Compares 2 independent groups
- Wilcoxon Signed-Rank Test: Tests differences between 2 related groups
- Chi-Square Test: Compares observed and expected results
Types of Data
- Categorical Data: Divided into groups, non-numerical (e.g., gender, color)
-
Numerical Data: Involves counting or measuring
- Discrete Data: Counted values, not divisible
- Continuous Data: Measured values, infinite possibilities
Data Distribution
- Normal Distribution: Data clusters around a central value, bell-shaped curve (e.g., human height)
-
Skewed Distribution: Data unevenly spread
- Positive Skew (right-skewed): Low values with a few high values
- Negative Skew (left-skewed): High values with a few low values
Nature of Observations
- Paired Observations: Data points connected from the same source (e.g., pre- and post-treatment measurements)
- Unpaired Observations: Independent data points from different groups (e.g., test scores from different schools)
Statistical Techniques
-
Descriptive Statistics: Summarizes and characterizes data without generalizing to a population
- Key metrics include averages, ranges, and standard deviations
-
Inferential Statistics: Extends inferences from sample data to the overall population
- Allows researchers to make predictions and generalizations
Data Management
- Ethical collection: Informed consent, anonymization, secure storage
- Handling missing data:
- Data integrity: Cleaning and screening
Data Presentation
- Visuals: Tables, graphs, and charts enhance clarity
- Best practices: Clear labeling, logical organization, relevant statistical notations
Importance of Data Collection Methods
- Quality and Accuracy: Impacts the precision and reliability of results
- Significance, Credibility and Consistency: Ensures information is aligned with research objectives, accurately measures outcomes, and is reproducible
- Elimination of bias and Generalizability: Lessens research biases, ensures a representative sample
- Data-driven Decision Making: Creates a strong basis for making well-informed decisions
Data Gathering Techniques
- Wide range of approaches and tools for obtaining quantitative and qualitative data
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Description
This quiz covers the essential concepts of statistical tests, including the distinction between parametric and non-parametric tests. Explore various tests like regression and correlation analyses, as well as data types such as categorical and numerical data. Enhance your understanding of how these tools apply to data evaluation and interpretation.
