• At Hypothesis and T-Distribution we discussed about hypothesis testing. We had talked about one sample and two sample t test.
• Contrast analysis is more general case of that.
• It allows us to make comparison of combination of groups :
• Can’t we combine them to form just two groups :
  • We want to preserve individual identity of group
    • Group with large no of samples should not dominate group with small no of samples
• Examples
  • Groups for which the context at test matches the context during learning (i.e., is the same or is simulated by imaging or photography) will perform better than groups with a different or placebo contexts. [2]
  • Group 1 is different from Group 2-3-4
    • 3 types of smile vs 1 neutral [3]
  • Group 1 and 4 are different from group 2,3

• Intuition behind formula of standard error
  • Sum of square of combined group is some of individual SoS when groups are independent
• α should be equal to desired confidence (0.90, 0.95 etc). It is divided by two because it is two sided

Reference

[1] : http://www.youtube.com/watch?v=yq_yTWK4mNs

[2] : https://pdfs.semanticscholar.org/c0ba/1c28b0e120a459820bfb20d430fa442ebd96.pdf

[3] : http://www.onlinestatbook.com/case_studies_rvls/smiles/index.html