1. Descriptive and inferential statistics Flashcards
Classify these variables as NOMINAL or CONTINUOUS:
A) Age
B) Gender
C) Height
A) Age = Continuous
B) Gender = Nominal
C) Height = Continuous
Describe what a confounding variable is
A variable that affects the outcome being measured as well as, or instead of, the independent variable.
- because a confounding variable is an unforeseen and unaccounted-for variable that jeopardizes reliability and validity of an experiment’s outcome
If a test is valid, what does this mean?
The test measures what it claims to measure
If a test is reliable what does this mean?
The test will give consistent results.
The discrepancy between the numbers used to represent something that we are trying to measure and the actual value of what we are measuring is called:
Measurement error
What is the ‘fit’ of the model?
The ‘fit’ of the model is the degree to which a statistical model represents the data collected
What is variance?
The variance is the average error between the mean and the observations made
A frequency distribution in which low scores are most frequent (i.e. bars on the graph are highest on the left hand side) is said to be:
Positively skewed
How can we compensate for practice effects?
Counterbalancing
How can we compensate for boredom effects?
Giving participants a break between tasks
Variation due to variables that have not been measured is known as:
Unsystematic variation
- Unsystematic variation results from random factors that exist between the experimental conditions (such as natural differences in ability, the time of day, etc.)
What is the assumption of homogeneity of variance?
That the variance within each of the populations is equal.
Variation due to the experimenter doing something in one condition but not in the other condition is known as:
Systematic variation
What does residual variance tell us?
Residual variance helps us confirm how well a regression line that we constructed fits the actual data set. The smaller the variance, the more accurate the predictions are
The purpose of a control condition is to
Allow inferences about cause
- A properly constructed control condition provides you with a reference point to determine what change (if any) occurred when a variable was modified
What helps to control for participant characteristics (thus minimize unsystematic variation)?
Randomization
How are Z scores calculated?
By subtracting the mean from the score and dividing the answer by the standard deviation
SCORE - MEAN = X
X / STDEV = Z-SCORE
The standard deviation is the square root of the:
Variance
What is the coefficient of determination?
A measure of the amount of variability in one variable that is shared by the other
Calculated as:
correlation coefficient squared
Complete the following sentence:
A large standard deviation (relative to the value of the mean itself)…
Indicates that the data points are distant from the mean
(i.e. the mean is a poor fit of the data).
The probability is p = 0.80 that a patient with a certain disease will be successfully treated with a new medical treatment. Suppose that the treatment is used on 40 patients. What is the “expected value” of the number of patients who are successfully treated?
32
because 80% of 40 patients is 32 (or 40 x .80 = 32)
What is the Confusion of the inverse?
A logical fallacy whereupon a conditional probability is equated with its inverse
- that is, given two events A and B, the probability of A happening given that B has happened is assumed to be about the same as the probability of B given A, when there is actually no evidence for this assumption.
More formally, P(A|B) is assumed to be approximately equal to P(B|A).
The test statistics we use to assess a linear model are usually _______ based on the normal distribution.
Parametric tests
What are the assumptions of the general linear model?
Independence:
- The errors in your model should not be related to each other
Additivity/Linearity:
- If you have several predictors then their combined effect is best described by adding their effects together
- The outcome variable is, in reality, linearly related to any predictors
Normality:
- The core element of the
Assumption of Normality asserts that the distribution of sample means (across independent
samples) is normal.
(In technical terms, the Assumption of Normality claims that the sampling
distribution of the mean is normal or that the distribution of means across samples is normal)
Homogeneity of variance:
- When testing several groups of participants, samples should come from populations with the same variance
