The Tao of Statistics: A Path to Understanding (with No Math) Book

1	The beginning - the question	1
2	Ambiguity - statistics	2
3	Fodder - data	6
4	Data - measurement	10
5	Data structure - levels of measurement	12
5.A	Nominal	14
5.B	Ordinal	16
5.C	Interval	18
5.D	Ratio	20
6	Simplifying - groups and clusters	22
7	Counts - frequencies	26
8	Pictures - graphs	28
9	Scatterings - distributions	30
10	Bell-shaped - the normal curve	34
11	Lopsidedness - skewness	38
12	Averages - central tendencies	40
12.A	Mean	42
12.B	Median	44
12.C	Mode	46
13	Two types - descriptive and inferential	48
14	Foundations - assumptions	52
15	Leeway - robustness	54
16	Consistency - reliability	56
17	Truth - validity	60
18	Unpredictability - randomness	64
19	Representativeness - samples	66
20	Mistakes - error	70
21	Real or not - outliers	72
22	Impediments - confounds	74
23	Nuisances - covariates	78
24	Background - independent variables	82
25	Targets - dependent variables	84
26	Inequality - standard deviations and variance	86
27	Prove - no, falsify	90
28	No difference - the null hypothesis	92
29	Reductionism - models	96
30	Risk - probability	98
31	Uncertainty - p values	100
32	Expectations - chi-square	104
33	Importance vs. difference - substantive vs. statistical significance	106
34	Strength - power	108
35	Likely range - confidence intervals	110
36	Association - correlation	112
37	Predictions - multiple regression	114
38	Abundance - multivariate analysis	118
39	Differences - t tests and analysis of variance	120
39.A	ANOVA	124
39.B	ANCOVA	126
39.C	MANOVA	128
39.D	MANCOVA	130
40	Differences that matter - discriminant analysis	132
41	Both sides loaded - canonical covariance analysis	134
42	Nesting - hierarchical models	136
43	Cohesion - factor analysis	138
44	Ordered events - path analysis	142
45	Digging deeper - structural equation models	144
46	Fiddling - modifications and new techniques	146
47	Epilogue	148