STATISTICAL TESTS

• Parametric tests


  Assume a normal distribution
• Non-parametric tests


  Do not assume a normal distribution
• Parametric tests


  Use interval data
• Non-parametric tests


  May use nominal or ordinal data
• Parametric tests


  Assume homogeneity of variance
• Non-parametric tests


  Do not assume homogeneity of variance
• Parametric tests are more powerful than non-parametric tests
• Factors determining choice of statistical test

  • Is it a test of difference or a test of association?
  • Is the design independent measures, repeated measures or matched pairs?
  • Is the data nominal, ordinal or interval?
• Nominal data is data arranged in categories
• Ordinal data is data which can be ranked without each value being equal in measurement
• Interval data is data where the intervals between each value are equal in measurement
• Tests of difference


  • Sign Test
  • Wilcoxon test
  • Related t-test
  • Unrelated t-test
• Tests of association


  • Chi-squared
  • Spearman's rho
  • Pearson's r
• Bella has conducted research using elite athletes as her sample. She measured the body temperature of her participants who had either just run 10K or had rested for 30 minutes. Bella wants to carry out a parametric test on this data.
• Bella should be able to carry out a parametric test as her data is interval (temperature measurements have distinct and equal intervals between each measurement); she can expect to see a normal distribution of data (because all of the participants are elite athletes they are likely to experience similar body temperatures during exercise or when resting; plus body temperature is a relatively stable variable). She can also expect homogeneity of variance (the standard deviations per condition are likely to be similar due to the nature of the sample – all elite athletes who are likely to have similar levels of health and fitness).