Statistical analysis is the science of collecting data and uncovering patterns and trends. It plays a crucial role in sports studies by enabling researchers to summarise data, identify key measures, calculate variations, predict future outcomes, and test hypotheses. Understanding these aspects allows for more accurate and reliable research findings.

Basic Terminology

• Variable: An attribute or characteristic that can take on different values.

• Dependent Variable: The outcome (response) variable that is measured in an experiment.

• Independent Variable: The variable that is manipulated in an experiment (consciously and purposefully by a researcher).

• Normality: The condition of data being normally distributed. The Gaussian distribution, is a probability distribution that appears as a bell curve.

• Parametric: Statistical methods that assume data follows a certain distribution.

• Factor: An independent variable in an experiment.

• Hypothesis: A statement that can be tested scientifically.

By understanding these concepts and how they apply to statistical analysis, researchers in sports studies can conduct more precise and valid research, leading to meaningful and actionable insights.

Definition of Statistical Analysis

Statistical analysis involves several key steps after data collection:

• Summarising Data: This can include creating visual representations such as pie charts, which fall under measures of distribution or frequency.

• Finding Key Measures of Location: These measures, such as the median and mean, are known as measures of central tendency.

• Calculating Measures of Spread: This includes determining whether data are tightly clustered or spread out, using range (R), standard deviation (SD), and interquartile range (IQR). These are measures of variation and position.

• Making Future Predictions: Based on past data, statistical analysis can help predict future trends.

• Testing Hypotheses: Hypothesis testing is used to either reject or fail to reject a null hypothesis (H₀).

• If $p \le 0.05$, results are statistically significant, and H₀ is rejected.

• If $ p > 0.05$, results are not statistically significant, and we fail to reject H₀.

Note: you cannot accept H₀. Only reject or fail to reject H₀, but after rejecting H₀ $(p \le 0.05)$ you can add – we can accept H_a.