4.4.1 Interpreting data

• Numerical data that can be measured objectively is known as quantitative
• What are the three main types of data in physical education?

  Quantitative, qualitative, mixed methods
• What type of data combines numerical measurements with descriptive feedback?

  Mixed methods
• Match the type of data with its description:

  Quantitative ↔️ Numerical data measured objectively

  Qualitative ↔️ Descriptive data on experiences

  Mixed Methods ↔️ Combines quantitative and qualitative data
• Ordinal data has a clear order or ranking.


  True
• Identifying the correct scale of measurement is crucial for statistical analysis.

  True
• Outliers in data may indicate measurement errors or unique circumstances.


  True
• Analyzing trends, outliers, relationships, and variability helps in drawing meaningful conclusions from data.


  True
• What is the definition of quantitative data in physical education?

  Numerical data measured objectively
• Identifying the type of data is crucial for selecting appropriate analytical tools in physical education.


  True
• Identifying the appropriate scale of measurement is crucial for selecting the right statistical analysis techniques.

  True
• Analyzing trends and patterns is essential for improving athlete performance and evaluating program effectiveness.


  True
• Arrange the following statistical tests from simplest to most complex:

  1️⃣ T-test

  2️⃣ Chi-square Test

  3️⃣ ANOVA
• Correlation analysis measures the strength and direction of linear relationships
• Trends and patterns in data can show increases, decreases, or consistent values
• What is a strong positive correlation between strength training and sprint times likely to indicate?

  Focus on strength development
• Speed in a 100m sprint is an example of quantitative
• What are the four scales of measurement used in physical education?

  Nominal, ordinal, interval, ratio
• What type of numerical data has equal intervals but no true zero point?

  Interval
• What are three key features to identify when analyzing data in physical education?

  Trends, outliers, relationships
• What type of relationship is examined when assessing how training load influences athletic performance?

  Correlation
• Why is analyzing trends and patterns in physical education data important?

  To optimize athletic performance
• Qualitative data provides insights into experiences and opinionsopinions
• Match the measurement scale with its description:

  Nominal ↔️ Categorical data with no inherent order

  Ordinal ↔️ Categorical data with clear order

  Interval ↔️ Numerical data with equal intervals but no true zero

  Ratio ↔️ Numerical data with a true zero
• Trends and patterns in data can show increases, decreases, or consistent values over time
• What is a trend in physical education data?

  General direction of data
• A t-test compares the means of two groups.

  True
• What does the chi-square test determine in physical education?

  Association between categorical variables
• What is an outlier in physical education data?

  Data point significantly different
• Qualitative data provides insights into experiences and opinions.


  True
• An athlete's self-reported comfort level during training is an example of qualitative data.


  True
• Categorical data with no inherent order is measured on a nominal
• Height, weight, and speed are examples of data measured on a ratio
• Increases, decreases, or consistent values in data are known as trends
• The spread or dispersion of data is known as variability
• A consistent improvement in sprint times over a training program indicates a positive trend
• What does mixed methods data combine in physical education?

  Quantitative and qualitative data
• Which scale of measurement has a true zero point in physical education?

  Ratio
• What are outliers in physical education data?

  Data points significantly different
• Graphs are used to visually represent data trends