Adding numbers is an important procedure in statistics. Instead of saying "add up all of the scores," we use the symbol
E (sigma)
Measures of central tendency are measures of
location
With respect to other scores in a distribution, measures of central tendency are
the points around which most of the scores are located
In order to decide which measure of central tendency is appropriate, you must first determine
the scale of measurement being used and the shape of the distribution
The mode is the appropriate measure of central tendency when the scale of measurement is
nominal
Which measure of central tendency should a researcher use to describe the sex of participants in a study?
the mode
Which measure of central tendency is appropriate if the shape of the distribution is severely skewed?
the median
The median is the preferred measure of central tendency when
the scale of measurement is ordinal
Why is the median unaffected by extreme scores occurring in only one tail of the distribution?
because the median does not take into account the actual values of all the scores
Which measure of central tendency should an academic counselor use to describe a student's rank order in his/her classes?
the median
The mean is defined as
the mathematical center of the distribution
To obtain the mean, we would
add all the scores and divide by the total number of scores
Which measure of central tendency is appropriate if the shape of the distribution is symmetrical and the measurement scale is interval or ratio?
the mean
The mean is the preferred measure of central tendency when
the distribution is symmetrical and the scale of measurement is interval or ratio
An experimenter investigated the ability to concentrate as a function of crowding. Concentration was measured as the amount of time it took the participant to complete a word puzzle. How should the experimenter summarize the scores on the dependent variab
Find the mean amount of time it took to solve the puzzle, if time scores are normally distributed
The mean is used most often in behavioral research because researchers tend to
measure variables that have interval or ratio scores, and the scores form approximately normal distributions
The sum of the deviations around the mean always equal
0
The best predictor of an individual score in a sample of scores is the
mean of the sample of scores
When using the mean to predict scores, error is represented by
the deviation of a score from the mean
A deviation score is more important than a raw score because it
gives the score's location relative to the mean
When we graph the results of an experiment, the Y axis indicates the
measure of central tendency we have used for the dependent variable
When we graph results from an experiment, a line graph is appropriate when
the independent variable is interval or ratio
If you see the notation EX^2 you should
divide all the Xs by 2
If you see the notation (EX)^2 you should
sum all the Xs, then square the sum
Measures of variability are used to
summarize and describe the extent to which scores in a distribution differ from one another
The term variability is most opposite to
consistency
When computing the variance, why do we square the deviations from the mean?
to compensate for the fact that deviations about the mean always sum to zero
Variance is defined as
average of the squared deviations around the mean
If the variance for a sample is computed and it is found to be rather large, the numbers
are spread around the mean
Standard deviation is defined as the square root of the
average of the squared deviations around the mean
The standard deviation is always
the square root of the variance
In roughly normal distributions, the standard deviation is approximately
one-sixth of the range
Adding a constant to or subtracting a constant from each of the scores in a distribution
does not change the value of the standard deviation
A psychology professor wanted to describe his/her class in terms of the personality characteristics of introversion/extroversion. The population variance was estimated to be 2.56. What does this mean?
When the professor uses the population mean to predict individuals' scores, he/she should expect to be in error by about 2.56.
The quantity N-1 has a special name. It is known as the
degrees of freedom
If we are going to predict future performance on the basis on the basis of a sample mean and sample standard deviation, it is describable to have a
small standard deviation
The proper way to describe errors of prediction is to compute
the variance
In the language of statistics, when we know that a relationship exists between two variables, we can use knowledge of that relationship to
account for the variance
The proportional improvement that results from using the relationship between two variables to predict scores compared with not using the relationship to predict scores is called
the proportion of variance accounted for
The absolute value of a number is the
numeric magnitude of the number, regardless of whether it is positive or negative
An evaluation of where a score is located in relation to the other scores in the distribution reflects its
relative standing
The z-score transformation is a useful statistical tool because it enables statisticians to
compare and interpret scores from virtually any distribution
Z-scores can be calculated from
relative location in a distribution
When the standard deviation of a raw score distribution is large, the corresponding z-scores distribution will be
relatively spread out
A z-score of zero always means that
the raw score is equal to the mean
Given a normal distribution, as z-scores' absolute values increase, those z-scores and the raw scores that correspond to them occur
less frequently
The distribution of z-scores is always
the same as the distribution of raw scores
When two normal z-distributions are plotted on the same graph, what can we say about the relative frequency of each z-score?
it will always be the same
The proportion of the total area under a normal curve between two z-scores corresponds to the _____ of that range of scores
relative frequency
A theoretically perfect normal curve, which serves as a model of the perfect normal z-distribution, is called the
standard normal curve
We can use the standard normal curve as our model for
any approximately normal distribution, when transformed to z-scores
In sampling distributions, all the sample contain sets of raw scores
from the same population
Which of the following statements accurately describes the sampling distribution of means?
The distribution of all possible sample means when an infinite number of samples of the same size N are randomly selected from one raw score population
A sampling distribution is an approximately normal distribution
regardless of the shape of the raw score distribution
Sampling distributions of means are always
approximately normally distributed
The mean of the sampling distribution of means is always
equal to the population mean
According to the central limit theorem, the sampling distribution of means always approximates a ____ distribution
normal
The mean of the sampling distribution always equals
the mean of the underlying raw score population
The standard deviation of the sampling distribution of means is called the
standard error of the mean