Statistical Models Flashcards
A statistical model gives a simple
description of relationships in the dataset
A statistical model uses
maths to summarise a dataset relative to multiple variables.
——— statistics use statistical models
inferential
Correlation is an example of a
statistical model
Data = model + ……….
error
error is sometimes called
residuals
The smaller the error, the better the model —–
“fit”.
A z-score describes
the position of a raw score in terms of its distance from the mean, when measured in standard deviation units.
The z-score is —— if the value lies above the mean, and —— if it lies below the mean. A z-score of 1 = 1SD away from the mean.
positive, negative
z-scores tell how an
individual sits within a distribution.
whats the difference between a hsitogram and a density plot
a histogram shows the counts of values in each range, while a density plot shows the proportion of values in each range.
The p value is a measure of
probability
IT IS A DECISION MAKING TOOL
its NOT
-probability that null hypothess is true,
-the probability you are making the wrong deciion
-It is not the probably that if you ran the study again, you would obtain the same result that % of the time.
-It does not mean you found an important effect.
-It does not reflect the size of the effect.
Pvalue is a ——– assessment
Dichotomous assessment.
Pvalue is a dependant on
Dependent on your alpha value and your p value.
The alpha is typically set at
0.05 meaning that p<.05 is deemed statistically significant.
In setting your alpha value you are balancing
Type I and Type II errors.
Effect size
Measure of the strength of the effect.
There are different ways to measure effect size,
e.g. r2, Cohen’s d.
A statistical hypothesis test is a test of
the statistical hypothesis, not the research hypothesis.
the goal behind statistical hypothesis testing is not to eliminate errors, but to
minimise them.
. A statistical test is pretty much the same. The single most important design principle of the test is to control the probability of a type I error, to keep it below some fixed probability. This probability is called the
significance level of the test
A “powerful” hypothesis test is one that has a small value of ——, while still keeping —- fixed at some (small) desired level.
beta, alpha
critical values since they define the
edges of the critical region
If the data allow us to reject the null hypothesis, we say that “the result is
statistically significant
