Psych Test 1

Question 1

Descriptive Statistic

Answer 1

Used to organize or summarize a set of data

Question 2

Inferential Statistic

Answer 2

Used to make interpretations based on data
example: Which brand of cola do people prefer?

Question 3

Sample

Answer 3

a small subset of a population that is
intended to be representative of the larger population

Question 4

Population

Answer 4

a group of people or things that have
some critical characteristic in common

Question 5

Raw Score

Answer 5

a datum point or value that has not been altered in any way. Raw scores are original measurements from surveys, tests, or other instruments that have not been weighted, transformed, or converted into any other form. Raw scores are also called observed scores.

Question 6

Random Sampling

Answer 6

In principle, all members of the population are equally likely
to become part of the sample
* Hard to do this unless the population is small and manageable

Question 7

Convenience Sampling

Answer 7

Sample from among individuals who are conveniently available to study

Question 8

independent variable

Answer 8

A variable that the experimenter manipulates
example: suppose you want to know how the time to
find an object is affected by the number of other objects present
* IV = number of objects present

Question 9

quasi-independent variable

Answer 9

A variable that the researcher thinks will predict scores on the DV, but that the researcher does not
manipulate

Question 10

dependent variable

Answer 10

A variable that the experimenter measures
examples: Response time, Accuracy, Score on Depression Scale

Question 11

continuous variable

Answer 11

A theoretically infinite number of values exist between adjacent units
example: reaction time, length, age

Question 12

discrete variable

Answer 12

No values exist between adjacent units
* Data are typically whole numbers or category labels
* e.g., college major; number of correct responses on an exam; number of people in household

Question 13

frequency distribution

Answer 13

Shows the number of times each score occurs within a data set

Question 14

cumulative frequency distribution

Answer 14

is calculated by adding each frequency from a frequency distribution table to the sum of its predecessors. The last value will always be equal to the total for all observations, since all frequencies will already have been added to the previous total

Question 15

percentile

Answer 15

values that divide a set of observations into 100 equal parts. The percentile rank is the proportion of values in a distribution that a specific value is greater than or equal to

Question 16

percentile rank

Answer 16

?

Question 17

scatterplot

Answer 17

Used to represent relationship between two measured variables

Question 18

Bar Graph

Answer 18

Categories (levels of the independent variable) are located on the X axis

Question 19

Histogram

Answer 19

Like bar graphs, but used to represent frequency distributions composed of interval or ratio data
- the bars touch each other

Question 20

frequency polygon

Answer 20

Very similar to histogram
*Point is plotted at mid-point of each interval
*Points are joined with a line
*Extended to intervals just above and just below end
points of the distribution

Question 21

positively skewed distribution

Answer 21

most of the scores are found at the
lower values
- looks like this: n___

Question 22

negatively skewed distribution

Answer 22

most of the scores are found at the
higher values
- looks like __n

Question 23

Mean

Answer 23

Score below which 50% of the scores in the distribution are found
* i.e., the median splits the distribution exactly in half
* Can be calculated in multiple ways

Question 24

Median

Answer 24

Score located at the exact mathematical center of a distribution
* Calculated by adding all the scores and dividing by the number
of scores:

Question 25

Mode

Answer 25

number that occurs the most often

Question 26

Range

Answer 26

highest minus lowest score

Question 27

Variance

Answer 27

is SD squared

Question 28

Standard deviation

Answer 28

the amount by which a score differs from the mean

Question 29

The scales that variables can be measured using:

Answer 29

nominal scale
ordinal scale
interval scale
ratio scale

Question 30

Nominal Scale

Answer 30

used to indicate category membership only.
examples: hair color, occupation, major, preferred cuisine

Question 31

Ordinal Scale

Answer 31

rank an order. they are also categories but they represent relative magnitudes
example: singing ability of favorite 5 artists

Question 32

Interval Scale

Answer 32

each score represents an actual value
example: fahrenheit scale
can include zero but not a true zero
example: 0 degrees F foes not = the absence of heat

Question 33

Ratio Scale

Answer 33

Similar to an interval scale, except that it contains a true zero, and does not contain negative values
ex: kelvin temp scale, exam score, length
- can make ratio statements: the length of A is twice the length of B

Question 34

What is the difference between descriptive statistics and inferential statistics

Answer 34

Descriptive statistics describes sets of data.
Inferential statistics draws conclusions about the sets of data based on sampling.

Question 35

Why do we study samples instead of directly studying populations?

Answer 35

quicker and more time efficient

Question 36

What is the most important reason that we wish to learn about the properties of samples?

Answer 36

To draw inferences about the larger population. If this is true of the sample, then it is also likely to be true of the population.

Question 37

What is random sampling, and why is it so important?

Answer 37

Random sampling ensures that results obtained from your sample should approximate what would have been obtained if the entire population had been measured. The simplest random sample allows all the units in the population to have an equal chance of being selected.

Question 38

Why does one manipulate an independent variable?

Answer 38

To determine whether changing the IV has any effect on the DV.

Question 39

Why do the bars in a bar graph not touch each other?

Answer 39

The intervals in a bar graph are not continuous

Question 40

What are the advantages and disadvantages of the mode, the median, and the mean?

Answer 40

Mode: Adavantages- Can be used with nominal variables, represents a score that actually did occur w/in the data, a score picked at random from the data set would be more likely to be equal to the mode than any other single value. Disadvantages- Many scores in a distribution may have the same frequency, mode doesn’t take into account any other scores in the distribution.
Median: Advantages- Relatively unaffected by extreme scores in the distribution, useful in situations in which extreme scores are possible and have no real significance, sometimes more meaningful than other measures, requires no assumptions about interval properties of the scale. Disadvantages- Based on counts and not values of scores, difficult to manipulate algebraically, not very stable from one sample to another.
Mean: Advantages- Reflects all the scores in the distribution, tends to be a better estimate of the population mean, can be manipulated algebraically and included in other equations. Disadvantages- All scores in the distribution contribute to it.

Question 41

Under what conditions might it be better to use the median than the mean to represent a
distribution?

Answer 41

The median is useful in situations in which extreme scores are possible, but have no real significance (highly skewed distributions).

Question 42

How does the mean compare to the median when representing a positively (or negatively)
skewed distribution?

Answer 42

the median is better to represent skewed distributions

Question 43

Why is the range typically not considered the best measure of variability?

Answer 43

It is the simplest and the least informative measure of variability. It is based only on the most extreme scores, so it is not very informative about the distribution.

Question 44

Why can one not calculate standard deviation by simply summing the deviations of raw scores around the mean?

Answer 44

The sum of the deviation always equals zero, so the average deviation will always be zero. Also some deviations are negative.