# The PENIS of Statistics (L2 & L3) Flashcards

What does PENIS stand for?

Parameters Estimation Null Hypothesis Statistic Testing Intervals [of confidence] Standard error.

What are parameters?

Every model has parameters, represented by ‘b’. Regardless of the model, parameters are estimated using the same principles.

All parameters have: standard error, confidence interval & p-value.

What makes the mean a statistical model?

The value ascertained by the mean is hypothetical; it is not observed in the data.

Does the mean have error?

Yes, as the value obtained will not be correct for every person in the population. However, it has the ‘least error’ due to the squared scores deviating the least.

What do we assess how well a model accurately represents data?

Sum of squared error (or variance).

How do we assess how well a model accurately represents the population?

Standard error and confidence intervals.

What do we use the mean for?

To make predictions about the population.

What is ‘error’?

The deviation from what a model predicts (eg. the mean) and the actual data point (the observed value).

How do we calculate the sum of squared error?

SS = the sum of (score - mean)squared.

How do we calculate mean squared error?

s[squared] = the sum of (score - mean)squared / n-1

Note: why n-1? Because we are calculating for the population (see degrees of freedom).

What is standard deviation?

Variance in squared units.

How do we calculate standard deviation?

SD = squared root of mean squared error.

What do sum of squared error, variance and standard deviation all represent?

- The fit
- The variability
- The representation of the population
- Error.

What are degrees of freedom?

The freedom the experimenter has to randomly sample people from the population, in order to keep calculating the same mean.

Note: why n-1? As last person sampled is chosen so the mean continues to calculate a stated value, -1 removes this ‘person’ from the equation.

What is estimation through the method of least squares error?

Based on minimising squared error whereby we keep estimation the value of the mean until we find the lowest error for the parameter (b value)