Interpretation Frameworks

Interpreting the power

If the true [parameter in power statement], there is a [power] probability that the [researcher in context] will find evidence for [Ha in context].

Interpreting the p-value

In a world where the null hypothesis is true, there is a [p-value] probability of seeing [result in context] as [extreme or more extreme] as ours due to chance alone.

Standard deviation interpretation

A randomly selected [value in context] will typically vary from the mean by [standard deviation] units.

Interpreting S

On average, the prediction for [y in context] based on the nymber of [x in context] is off by___.

Interpreting r²/ coefficient of determination

__% of the change in [y in context] is explained by the linear relationship with [x in context].

Mean (expected value) interpretation

For many, many [subject/unit from which data is gathered in context], we would expect the average [value in context] to be [expected value/mean].

Interpreting the slope

The predicted [y in context] goes up/down by [slope] for each increase of 1 [x in context].

Interpreting the y-intercept

At zero [x in context], the [y in context] is predicted to be [y intercept] [y units].

Interpreting a residual

The actual value of the [x in context] is more/less [y in context] than predicted.

Interpeting probability as long-run relative frequency

We expect that [p%] of many [events equal to/similar to this one in context] will result in [desired outcome in context]

Interpeting a confidence interval

We are __% confident that the interval from_ to _ captures the true [parameter in context].

Interpreting a confidence level

__% of intervals created from many, many random samples of the same size from this population will capture the true [parameter in context].