Stats and graphs pysch review

Nominal-level variables

Cannot be quantified, placed in either descending or ascending order

Ordinal-level variables

Can be ranked from the lowest to the highest, but the intervals between ranks may be unequal.

Interval-level variables

Intervals are assumed to be equal and variables may have a zero value (ex: temperature, test scores)

Ratio-level variables

Same has interval levels, but zero is fixed and meaningful (weight, height)

Normal distribution

A special type of distribution that looks like a bell. Occurs naturally in many situations. IQ scores, height of people, age, and blood pressure are all normally distributed.

Mean

The average of all scores

Median

The “middle” of a sorted list of numbers

Mode

The value that appears most often in a data set

Standard deviation

An accurate measure of dispersion that considers all individual values and uses all information available in the data set low values indicate that the data points tend to be closely grouped around the main whereas high values indicate that data points are further spread out

Semi-interquartile range

Does not assume normality of distribution and can be used with ordinal level data

Inferential statistics

Allow us to make it possible to test hypothesis and make conclusion

Null hypothesis

Represents the idea of no difference or no effect

Alternative hypothesis

Proposes that there is a true effect or relationship in the population

Type l error

False positive. Occurs when we reject a null hypothesis.

Type 2 error

False negative. We failed to reject a no hypothesis that is actually false. The probability of missing a true effect or relationship.

Parametric tests

Tests that assume data is naturally distributed

Non parametric tests

Does not make the assumption that data is naturally distributed

Effect size

A measure of magnitude

Mann Whitney U test

Does not rely on any assumptions regarding the distribution of the variable, making it non-parametric

Histograms

Used to show the distribution of a variable in the population. Typically along the X axis, a histogram includes the possible scores of a variable can take, and along the Y axis, the number of people who got a particular score data is continuous.

Skewness

How slanted the distribution is towards the left or the right

Kurtosis

The pointiness versus flatness of distribution

Bar graph

Used to visualize results of experimental research that involves a comparison between groups or categories

Box and whisker plots

Makes it easy to see how data is spread out, shows outliers

Scatter plots

Useful to visualize the relationship between two variables when there is a positive or negative correlation. Visually, this will affect the scatter plot.