C Appendix: Symbols, formulas, statistics and parameters

Symbols used

TABLE C.1: Some symbols used
Symbol	Meaning	Reference
\(H_0\)	Null hypothesis	Sect. 28.2
\(H_1\)	Alternative hypothesis	Sect. 28.2
df	Degrees of freedom	Sect. 31.4
CI	Confidence interval	Chap. 21
s.e.	Standard error	Def. 18.3
\(n\)	Sample size
\(\chi^2\)	The chi-squared test statistic	Sect. 31.4

Confidence intervals

Almost all confidence intervals have the form

\[ \text{statistic} \pm ( \text{multiplier} \times \text{s.e.}(\text{statistic})). \] Notes:

The multiplier is approximately 2 for an approximate 95% CI (based on the 68–95–99.7 rule).
\(\text{multiplier} \times \text{s.e.}(\text{statistic})\) is called the margin of error.
Confidence intervals for odds ratios are slightly different, so this formula does not apply for odds ratios. For the same reason, a standard error for ORs is not given.

Hypothesis testing

For many hypothesis tests, the test statistic is a \(t\)-scores, which has the form:

\[ t = \frac{\text{statistic} - \text{parameter}}{\text{s.e.}(\text{statistic})} \] Notes:

Since \(t\)-scores are a little like \(z\)-scores, the 68–95–99.7 rule can be used to approximate \(P\)-values.
Tests involving odds ratios do not use \(t\)-scores, so this formula does not apply for tests involving odds ratios.
For tests involving odds ratios, the test statistic is a \(\chi^2\) score and not \(t\)-score. For the same reason, a standard error for ORs is not given.
The \(\chi^2\) statistic is approximately like a \(z\)-score with a value of (where \(\text{df}\) is the ‘degrees of freedom’ given in the software output):

\[ \sqrt{\frac{\chi^2}{\text{df}}}. \]

TABLE C.2: Some sample statistics used to estimate population parameters. Empty table cells means that these are not studied. The asterisk means that no formula is given in this text.
	Parameter	Statistic	Standard error	S.E. formula reference
Proportion	\(p\)	\(\hat{p}\)	\(\displaystyle\text{s.e.}(\hat{p}) = \sqrt{\frac{ \hat{p} \times (1 - \hat{p})}{n}}\)	Def. 20.2
Mean	\(\mu\)	\(\bar{x}\)	\(\displaystyle\text{s.e.}(\bar{x}) = \frac{s}{\sqrt{n}}\)	Def. 22.1
Standard deviation	\(\sigma\)	\(s\)
Mean difference	\(\mu_d\)	\(\bar{d}\)	\(\displaystyle\text{s.e.}(\bar{d}) = \frac{s_d}{\sqrt{n}}\)	Def. 23.2
Diff. between mean	\(\mu_1 - \mu_2\)	\(\bar{x}_1 - \bar{x}_2\)	\(\displaystyle\text{s.e.}(\bar{x}_1 - \bar{x}_2)\)
Odds ratio	Pop. OR	Sample OR	\(\displaystyle\text{s.e.}(\text{sample OR})\)
Correlation	\(\rho\)	\(r\)
Slope of regression line	\(\beta_1\)	\(b_1\)	\(\text{s.e.}(b_1)\)
Intercept of regression line	\(\beta_0\)	\(b_0\)	\(\text{s.e.}(b_0)\)
R-squared		\(R^2\)