STATISTICS

• Primary data


  Data collected at source e.g. from running an experiment, conducting a questionnaire
• Probability


  Measure of the level of significance to determine whether results are significant and not due to chance factors
• Secondary data


  Data that has not been collected at source, obtained by other researchers
• Significance level


  Decimal value where 'p' stands for the probability that chance factors are responsible for the results
• Meta-analysis


  1. Researcher conducts statistical analysis of quantitative findings from multiple published studies

  2. Combines findings to draw overall conclusion

  3. Results expressed in terms of effect size
• For most purposes in psychology, the 5% level of significance is appropriate which is expressed as p < 0.05
• Null hypothesis


  Hypothesis that is tested to determine if the observed results are significant
• Meta-analysis


  • Bias is reduced as researcher has not personally conducted original research
  • Reliability should be high as large number of studies analysed statistically
• Inferential statistics


  Enable us to draw inferences about the population
• Quantitative data


  Data in the form of numbers
• Descriptive statistics


  Can only tell us about the sample taken from the population
• One-tailed test


  The alternative hypothesis predicts the direction of difference
• Qualitative data


  Data in the form of words e.g. thoughts, feelings, attitudes, ideas, beliefs
• Two-tailed test


  The alternative hypothesis simply states that 'there will be a difference'
• Measures of central tendency


  Statistics that describe the central or typical value of a data set
• Type I Error


  Null hypothesis is rejected when it should have been accepted (false positive)
• Mean


  Calculates the average score of a data set
• Type II Error


  Null hypothesis is accepted when it should have been rejected (false negative)
• Mode


  Calculates the most frequently occurring score in a data set
• Using a 0.05 significance level guards against making either a Type I or a Type II Error
• Researcher sets probability level too high (e.g. 0.10)


  More likely to make a Type I Error
• Median


  Calculates the middle value of a data set
• Researcher sets probability level too low (e.g. 0.01)

  More likely to make a Type II Error
• Using statistical tables to determine significance


  1. Determine if test is one-tailed or two-tailed

  2. Identify N value (sample size)

  3. Identify significance level being applied (e.g. 0.05)
• Measures of dispersion

  Calculate the spread of scores and how much they vary from the mean or median
• Maguire's (2000) research using London taxi drivers clearly gets the thumbs up for passing the p < 0.05 test
• Range


  Difference between the lowest and highest scores in a data set
• Dr Stats concluded that oranges and beef do indeed increase IQ significantly using a significance level of 0.10
• Standard deviation


  Calculates how a set of scores deviates from the mean
• A Type I Error is likely to have occurred because the significance level of 0.10 has been set too high