Notes on Data Interpretation and Analysis

  • Introduction to Data Interpretation

    • Instructor: Paul Tooney

    • Contact: Paul.Tooney@newcastle.edu.au

  • Learning Objectives

    • Understand populations, sampling & sample size

    • Identify data presentation types and their strengths/limitations

    • Describe and compare data

    • Discuss data distribution types

    • Define measures of central tendency

    • Introduction to hypothesis and hypothesis testing

    • Importance of sample size & power calculation

  • Data Presentation

    • Tables: Display frequency counts and relative frequencies

    • Graphs:

    • Bar Charts: Show cases visually (e.g., diseases)

    • Histograms: Display distribution of data

    • Box and Whisker Plots: Visualize five-number summaries

    • Allows for easy trend identification

  • Concepts

    • Population: Entire group of interest

    • Sample: Subset drawn from the population

    • Statistics: Summarizing numerical values from a population/sample

  • Sampling Methods

    • Representative vs. convenience samples

    • Random sampling techniques to eliminate bias

  • Sample Size and Error

    • The optimal sample size depends on the context and accuracy requirements

    • Sampling error defined as the difference between sample and population statistics

  • Hypothesis Testing

    • Aim to determine the probability of observing the data assuming the null hypothesis is true

    • Null hypothesis (Ho) indicates no effect/difference

    • Alternative hypothesis (H1) suggests a difference exists

  • Measures of Central Tendency

    • Include mode, median, and mean

    • Dispersion: Indicates data spread (e.g., range, standard deviation)

  • Simple Linear Regression

    • Method to evaluate relationships between variables

    • Coefficient of determination (R²) indicates fit quality of the model

  • Applications of Statistics in Research

    • Statistical methods facilitate drawings conclusions from sample to population based on data