Evaluation of Statistical Hypotheses

Kinship and Prosocial Behaviour Postregistration

• DeBruine, Lisa M.
  • roles: Conceptualization, Data curation, Software, Writing - original draft, Writing - review & editing
  • email: lisa.debruine@glasgow.ac.uk
• Lakens, Daniël
  • roles: Conceptualization, Formal analysis, Writing - original draft, Writing - review & editing

Abstract

A reanalysis of data from DeBruine (2002) Facial Resemblance Enhances Trust, PRSLB.

Results

Hypothesis 1: self_pref

Cues of kinship will increase prosocial behaviour. Cues of kinship will be manipulated by morphed facial self-resemblance. Prosocial behaviour will be measured by responses in the trust game. The prediction is that the number of trusting AND/OR reciprocating moves will be greater to self morphs than to other morphs.

• t_lo is confirmed if analysis trust yields conf.int[1] > 0 The result was conf.int[1] = 0.021 (TRUE)
  
• t_hi is confirmed if analysis trust yields conf.int[2] > 0.2 The result was conf.int[2] = 0.979 (TRUE)
  
• r_lo is confirmed if analysis recip yields conf.int[1] > 0 The result was conf.int[1] = -0.509 (FALSE)
  
• r_hi is confirmed if analysis recip yields conf.int[2] > 0.2 The result was conf.int[2] = 0.426 (TRUE)

Corroboration (TRUE)

The hypothesis is corroborated if the 97.5% CI lower bound is greater than 0 and the 97.5% CI upper bound is greater than 0.2 (the SESOI) for either the trust or reciprocation moves.

(t_lo & t_hi) | (r_lo & r_hi)

Falsification (FALSE)

The hypothesis is falsified if the 97.5% CI upper bound is smaller than 0.2 (the SESOI) for both trust and reciprocation.

!t_hi & !r_hi

All criteria were met for corroboration.

Analyses

Analysis 1: trust

t.test(kin$trust_self, kin$trust_other, paired = TRUE, conf.level = 0.975)

• statistic:
  • t: 2.50454132981017
• parameter:
  • df: 23
• p.value: 0.0197947772076059
• conf.int:
  1. 0.0212945707630106
  2. 0.97870542923699
• estimate:
  • mean of the differences: 0.5
• null.value:
  • difference in means: 0
• stderr: 0.199637352376173
• alternative: two.sided
• method: Paired t-test
• data.name: kin$trust_self and kin$trust_other

Analysis 2: recip

t.test(kin$recip_self, kin$recip_other, paired = TRUE, conf.level = 0.975)

• statistic:
  • t: -0.213835558603476
• parameter:
  • df: 23
• p.value: 0.832559599965427
• conf.int:
  1. -0.508901684452692
  2. 0.425568351119359
• estimate:
  • mean of the differences: -0.0416666666666667
• null.value:
  • difference in means: 0
• stderr: 0.194853778944834
• alternative: two.sided
• method: Paired t-test
• data.name: kin$recip_self and kin$recip_other