**DATA TYPES**

**Quantitative**- Discrete
- Continuous

**Qualitative**

- Nominal
- Ordinal
- Binary

**Variable:**

Actual property measured by individual observations

**Variate:**

Single score or reading of a given variable

**Normal distribution** (ie Height):

**Mean:**

**Variance:**

**Standard Deviation:**

If the data are Normally distributed, the distribution of data can be described by two parameters:

**Mean****Standard deviation (or variance)**

Mean + / – 1 × SD = 68 %

Mean + / – 2 × SD = 95 % (more accurately 1.96 times SD)

Mean + / – 3 × SD = 99 %

A different mean shifts the curve along the x-axis, but does not alter its shape. When the standard deviation decreases, the curve becomes steeper; when the standard deviation increases, the curve becomes flatter.

**Not Normal distribution:**

**Range: **

Interval from lowest to highest value

**Interquartile range:**

Difference between upper and lower quartiles

**Mode: **

Most common category

**Median: **

Equal number of measurements above and below

A skewed curve is skewed to the right if the ‘tail’ is on the right side and skewed to the left in the ‘tail’ is to the left.

**Confidence interval:**

The sample mean is an unbiased estimator of the population mean.

**Central limit theorem:** the distribution of mean (from different samples) will be a Normal distribution, even if the samples or population are not Normally distributed.

The distribution of the mean is Normal with the sample mean as mean and the **standard error of the mean** (SEM) as measure of dispersion (n is sample size):

Confidence intervals can be constructed by the mean plus or minus the standard error of the mean:

Mean + / – 1 × SEM = 68 %

Mean + / – 2 × SEM = 95 % (more accurately 1.96 times SEM)

Mean + / – 3 × SEM = 99 %