Module 3: Statistical Reasoning in Everyday Life

Statistics

Using math to understand data

Descriptive statistics

Using statistical methods to provide a simple summary of data

Measures of Central Tendency

Scores that represent a whole distribution of scores

Mode

Frequently occurring scores (simplest measure)

Mean

Total sum of scores divided by number of scores (most common)

Median

The midpoint/halfway point across the range of scores

Skewed data

Distribution is lopsided/data clusters

Indication of the variation among data = data is reliable

more

Range

Difference between lowest and highest of scores

Standard deviation

Measurement of how much score differ from mean

Inferential statistics

Using results from a small portion of a group to understand everyone in that group

Statistically significant

Samples difference reflects true population difference

3 Principles when considering population and sample difference

1. Representative samples are better than biased samples

2. Bigger samples are better than smaller ones (sample many people)

3. More estimates are better than fewer estimates

2. Bigger samples are better than smaller ones (sample many people)

3. More estimates are better than fewer estimates

Null hypothesis

Initial assumption that no difference exists between groups (if difference is large enough: null hypothesis is rejected and replaced with an alternative hypothesis)

p-values

Indications of the probability of a result

If p<0.05 (p is less than 5%), it is classified as "____ ____"

very low

Practical significance

Real world importance (data could be statistically significant, but not practically significant

Normal curve

Symmetrical, bell-shaped curve, represents perfectly normal distribution