3321 exam 1 Flashcards
mostly textbook questions
Our ability to draw meaningful conclusions based on a sample statistic depends, in part,
on the _____ of our sample.
variability
. When we are engaged in drawing conclusions about a population we are using inferential statistics. (T or F)
True
Which one of the descriptions below about statistics and parameters is correct?
A statistic summarizes the data in a sample, but a parameter summarizes the data in a population
What is the practical distinction between discrete and continuous variables?
Discrete variables take on only a few different values, but continuous variables
can take on any value between the lowest and highest score.
Interval vs ratio scale
Both: differences between values is meaningful, but ratio has a meaningful zero: celsius vs kelvin. For ratio can say “twice as big”
where does the tail point for a positive skew?
to the right (positive end)
where does the tail point for a negative skew?
to the left (negative end)
nominal
name things
ordinal
ranking
Which typical measure of variability produces scores in the same units as the measurements themselves?
standard deviation
Why do we divide by n-1 in calculating the variance or standard deviation as an estimate of the population value?
Because once one knows the mean and n-1 of the scores, one already knows all the scores & Because dividing by n produces a biased estimate
The endpoints of an interval are called _____.
the real upper (and lower) limits
When will the median and mean be equal
when the distribution is symmetrical
when will the mode, median, and mean be the same
when the distribution is symmetric & unimodal
When would you use median as opposed to mean to measure central tendency?
to minimize the effect of extreme scores- median is more robust
advantages of using the mean to measure central tendency
The mean gives a more stable estimate of the central tendency of a population
over repeated sampling. The mean can be used algebraically.
what type of data is the mode most appropriate for
nominal
why do we use degrees of freedom for parameter estimations?
When you estimate parameters from data (like means or variances), the degrees of freedom help adjust for the fact that you are using sample data rather than population data. For instance, in calculating the sample variance, the degrees of freedom account for the fact that one parameter (the mean) is estimated from the same data.
additive law
Given a set of mutually exclusive events, the
probability of the occurrence of one event or another is equal to the sum of their
separate probabilities.
multiplicative law
The probability of the joint occurrence of
two or more independent events is the product of their individual probabilities.
frequentist vs analytic view of probability
analytic- simple, concrete terms. Frequentist- more abstract sampling terms
difference btwn mutually exclusive and independent
– independent if occurrence of one has no
impact on the other (dependent if related)
– mutually exclusive if one event precludes
occurrence of the other
critical value
It is the value of the test statistic beyond which you reject the null hypothesis.
type 1 error
rejecting the null when it is in fact true
type 2 error
failing to reject the null when it is false
what does the significance level define?
the probability of a type 1 error
what is power
the probability of rejecting the null when it is actually false
what is sampling error
The variability of sample estimates of some statistic such as the mean.
what is standard error of the mean
The standard deviation of the sampling distribution of the mean.
How can you increase power?
Increasing the distance between the mean of the null hypothesis distribution and the true distribution of scores
