We tried to follow those methods of psychological personality testing, and deliberately selected the most suitable characteristics that would explicitly display the differences: a) images of characteristics were consistent to the preceding academic studies; b) showed large differences in the academic studies and means were close to 50%; c) did not show extreme values; d) images of characteristics were consistent to the preceding surveys of other studies.

Analytical Strategy

Firstly, we confirm whether a single or two-item question made a difference in the preceding academic studies. We also focused our analysis on whether personality self-fulfillment was occurring: with data from participants who “have no knowledge of blood type personality” or “do not believe in the relationship” (hereinafter abbreviated as “no-knowledge group”).
Secondly, we confirm whether AI can predict a persons’ blood type more than chance. We made to reference a facial recognition article written by AI engineers (Nakamura & Iwasada, 2017) aimed at prediction because there was virtually no preceding research available to analyze personality with AI; it was for this reason we utilized AI as an experimental method.

Procedure

Our analytical methods on personality were as follows:
Analysis 1: ANOVA of blood type and personality – Surveys 1 and 2
Analysis 2: ANOVA of blood type and personality for “no-knowledge group” – Survey 2 only
We set the alpha level to 0.05. An analysis of variance (ANOVA) was performed with personality characteristics scores as the dependent variables and self-reported ABO phenotypes (A, B, O and AB). Before ANOVA analyses, the normality of distributions was checked for each personality characteristics score; this showed a normal distribution. Effect sizes (Cohen, 1977) were also calculated. The data were analyzed using jamovi software version 1.2.27 (The jamovi project).
Our analytical method on blood type predictions using AI was as follows:
Analysis 3: Blood type prediction using AI – Survey 2 only
All the data of survey 2 were stored in Amazon S3, and with Amazon Machine Learning, all 12 characteristics, including gender, age, and marital status, were used as training data for prediction targeting for the blood type (since Masahiko Nomi claimed that these elements affected personalities). Multinominal logistic regression algorithm was chosen for the prediction. We divided the whole data into five groups of same sample size. Each group was estimated as the prediction data, the rest four groups as the training data, and then the average of the five predictions was calculated. In these cases, since the sample sizes of the AI training data were small (this means that the prediction errors might become larger if we used the raw data of 1-year increment of age), a dummy variable of 10-year increments was used [20s = 2, 30s = 3, 40s = 4, 50s = 5].