Table 4.

Parameter distributions and statistical tests in Datasets 1 and 2

	σ2(0)	ω	log(ν)	log(κ1)
Dataset 1 (baseline, n = 80)
Nonclinical controls: mean (SD)	2.5 (3.9)	−1.3 (2.4)	4.1 (1.0)	−0.8 (1.4)
Psychotic: mean (SD)	3.0 (3.9)	−1.4 (2.0)	3.1 (1.1)	−0.2 (0.8)
Clinical controls: mean (SD)	1.4 (1.9)	−1.2 (2.0)	3.3 (1.3)	−0.1 (1.4)
Kruskal–Wallis χ2(2,80)	2.33, p = 0.31	0.22, p = 0.9	11.9, p = 0.003	9.6, p = 0.008
	η2 = 0.02	η2 = 0.0	η2 = 0.15	η2 = 0.12
Post hoc Dunn tests
Psychotic versus nonclinical controls	p(adj) = 0.3	p(adj) = 1	p(adj) = 0.002	p(adj) = 0.01
Clinical versus nonclinical controls	p(adj) = 0.2	p(adj) = 0.7	p(adj) = 0.01	p(adj) = 0.01
Psychotic versus clinical controls	p(adj) = 0.2	p(adj) = 0.5	p(adj) = 0.3	p(adj) = 0.4
Dataset 1 (follow-up, n = 55)
Nonclinical controls: mean (SD)	2.8 (3.4)	−0.9 (2.0)	3.6 (0.8)	−1.2 (1.1)
Psychotic: mean (SD)	3.2 (3.7)	−1.4 (1.5)	2.5 (1.2)	−0.3 (0.8)
Clinical controls: mean (SD)	1.2 (0.9)	−1.1 (2.0)	3.5 (1.1)	−0.5 (1.4)
Kruskal–Wallis χ2(2,80)	2.35, p = 0.3	2.32, p = 0.3	8.5, p = 0.01	8.0, p = 0.02
	η2 = 0.04	η2 = 0.04	η2 = 0.16	η2 = 0.15
Post hoc Dunn tests
Psychotic versus nonclinical controls	p(adj) = 0.4	p(adj) = 0.2	p(adj) = 0.01	p(adj) = 0.007
Clinical versus nonclinical controls	p(adj) = 0.2	p(adj) = 0.3	p(adj) = 0.5	p(adj) = 0.1
Psychotic versus clinical controls	p(adj) = 0.3	p(adj) = 0.3	p(adj) = 0.01	p(adj) = 0.1
Dataset 2 (n = 167)
Nonclinical controls: mean (SD)	3.1 (2.6)	−2.3 (2.0)	2.8 (1.0)	−0.8 (0.9)
Scz: mean (SD)	1.9(1.5)	−2.1 (1.8)	2.1 (1.2)	0.2 (1.0)
Mann–Whitney U test	Z = 3.1, p = 0.002, r = 0.24	Z = −0.6, p = 0.6, r = 0.04	Z = 3.9, p = 0.0001, r = 0.3	Z = −5.6, p = 3 × 10−8, r = 0.43
Dataset 2 (better-matched controls, n = 116)
Nonclinical controls: mean (SD)	2.8 (2.7)	−2.2 (2.1)	2.9 (1.1)	−0.6 (1.0)
Scz: mean (SD)	1.9 (1.5)	−2.1 (1.8)	2.1 (1.2)	0.2 (1.0)
Mann–Whitney U test	Z = 1.9, p = 0.056, r = 0.18	Z = 0.12, p = 0.9, r = 0.01	Z = 3.4, p = 0.0007, r = 0.31	Z = −4.1, p = 0.00004, r = 0.38