Use LEFT and RIGHT arrow keys to navigate between flashcards;
Use UP and DOWN arrow keys to flip the card;
H to show hint;
A reads text to speech;
130 Cards in this Set
- Front
- Back
|
Summarize data so they can easily be comprehended. A. Inferential statistics B. Differential statistics C. Descriptive statistics D. Frequency distribution |
C |
|
|
Helps researchers draw inferences about the effects of sampling errors on the results that are described with descriptive statistics. A. Inferential statistics B. Differential statistics C. Descriptive statistics D. Frequency distribution |
A |
|
|
Help researchers to make generalizations about the characteristics of populations on the basis of data obtained by studying samples. A. Inferential statistics B. Differential statistics C. Descriptive statistics D. Frequency distribution |
A |
|
|
A margin of error is a/n A. Inferential statistic B. Differential statistic C. Descriptive statistic D. Frequency distribution |
A |
|
|
Values obtained from a sample are called A. statistics B. parameters C. percentages D. errors |
A |
|
|
Values obtained from a population are called A. statistics B. parameters C. percentages D. errors |
B |
|
|
A statistic that describes how many participants per 100 have a certain characteristic A. statistic B. parameter C. percentage D. error |
C |
|
|
T or F When a researcher tests a random sample instead of all members of a population, the results will be the same as the results the researcher obtained from the population. |
F - random sampling produces sampling errors |
|
|
T or F A margin of error is associated with descriptive statistics. |
F - they are associated with inferential statistics |
|
|
T or F Significance tests are associated with descriptive statistics. |
F - associated with inferential statistics |
|
|
A common place where margin of error is used A. census B. significance testing C. polls D. populations |
C |
|
|
T or F A frequency distribution will look like a A. bar graph B. histogram C. line graph D. pie chart |
C. |
|
|
A frequency polygon consists of A. the space above the line B. the space below the line C. all of the negative values D. all of the positive values |
B |
|
|
T or F A larger sample size will create a smoother curved line on a frequency distribution. |
T |
|
|
A smooth curve highest in the middle and tapering on either side is called
A. a normal curve B. a positive skew C. a negative skew D. skewed to the left E. skewed to the right |
A |
|
|
A frequency distribution with the highest point towards the left and a tail to the right is called A. a normal curve B. a positive skew C. a negative skew D. skewed to the left E. skewed to the right |
B and E |
|
|
A frequency distribution with the highest point to the right and a tail to the left is called a A. a normal curve B. a positive skew C. a negative skew D. skewed to the left E. skewed to the right |
C and D |
|
|
The most frequently used average or balance point in a distribution A. mean B. median C. mode D. range |
A |
|
|
T or F To obtain the mean one puts the data in value order and selects the value of the midpoint. |
F - that is how to get the median. To get the mean one adds all values and divides by the number of values - it is an average |
|
|
The value around which the deviations sum to zero A. mean B. median C. mode D. range |
A. This is the formal definition of “mean” |
|
|
T or F A deviation is obtained by subtracting the mean from a data point. |
T |
|
|
The sum of the deviations for any group should equal A. 0 B. 2 C. 8 D. 1 |
A |
|
|
Identify a drawback of the mean. |
It is drawn in the direction of extreme scores. |
|
|
When a distribution is highly skewed researchers will use the A. mean B. median C. mode D. range |
B |
|
|
T or F A good way to remember that median is the middle number is to also remember that a median is in the middle of a road. |
it helps me, not sure if it helps you |
|
|
Putting scores in order from low to high and counting to the middle gives one the A. mean B. median C. mode D. range |
B |
|
|
The most frequently occurring score is the A. mean B. median C. mode D. range |
C |
|
|
T or F The mode is often used in formal research reports |
F |
|
|
T or F You can have more than one vale for the mode |
T |
|
|
The term “average” refers to A. mean B. median C. mode D. range |
A, B, and C |
|
|
A synonym for the term “averages” is A. mean B. measures of middle tendencies C. measures of central tendencies D. measures of average tendencies |
C |
|
|
Which two measures are most often used to describe a set of scores? A. Range and mean B. Standard deviation and range C. Standard deviation and mean D. Frequency distribution and mean |
C |
|
|
What variables are used to describe standard deviation for a population? A. x & y B. S & SD C. s & sd D. dev & int |
B |
|
|
What variables are used to describe standard deviation for a sample? A. x & y B. S & SD C. s & sd D. dev & int |
C |
|
|
This refers to the amount by which participants differ from each other A. variability B. random integer C. distribution D. central tendency |
A |
|
|
T or F As a rule the smaller the variability, the larger the standard deviation |
F |
|
|
For a normal distribution _____ of the participants lie within one standard-deviation unit of the mean. A. 10% B. 33% C. 50% D. 68% |
D |
|
|
T or F The normal curve is one of the great inventions of statisticians. |
F - it is a reflection of nature |
|
|
T or F It is possible for two groups to have the same mean but different standard deviations. |
T |
|
|
When data is ordinal, which measure of central tendency is used? A. mean B. median C. mode |
B |
|
|
When the mean is used to report the average, it is also customary to report A. standard deviation and mean B. range and mean C. variability quotient D. range or interquartile range |
D |
|
|
If median describes the central tendency of a set of scores, interquartile range describes its A. margin of error B. reliability C. variability D. average |
C |
|
|
How is the range of a set of scores calculated? |
Take the score of the highest response and subtract the score of the lowest response. |
|
|
How would one find the IQR of a set of data? |
IQR = interquartile range Divide an ordered list of responses into quarters. Subtract the value of the marker at the first quarter from the market at the third quarter. |
|
|
T or F Interquartile range is considered more reliable than range. |
T |
|
|
A researcher drew random samples of engineers and psychologists and administered a self-report measure of sociability, then computed the mean for each group. The mean for the engineers was 65.00 and the mean for the psychologists was 70.00. How many possible explanations are there for the difference? A. 1 B. 2 C. 3 D. 42 |
C |
|
|
What does null hypothesis say about sampling error? |
A null hypothesis states that there is no significant difference between specified populations so any difference in the experiment are there due to either sampling or experimental error. Therefore a null hypothesis calls into question the sampling. |
|
|
Does the term “sampling error” refer to “random errors” or “bias”? |
Random errors only. |
|
|
The null hypothesis states that the true difference between two groups equals what numerical value? A. -1 B. 0 C. 1 D. .001 |
B |
|
|
Significance tests determine if ______ is/are true. A. the interval and ratio levels B. the null hypothesis C. the hypothesis D. the dissertation |
B |
|
|
The expression p = <.05 means what? A. the probability is unlikely B. the null hypothesis is rejected C. the study is statistically significant D. all of the above |
D |
|
|
T or F Researchers reject the null hypothesis when the probability of its truth is high. |
F - probability is low |
|
|
What do researchers do if the probability is greater than .05? |
They fail to reject the null hypothesis and it remains as a possibility for the explanation of the difference. |
|
|
The Pearson product-moment correlation coefficient is represented by the variable A. p B. n C. r D. M |
C |
|
|
When researchers study the relationship between two quantitative sets of scores (interval or ratio level data) they compute a A. measure of variability B. measure of central tendency C. interquartile range D. correlation coefficient |
D |
|
|
When the high points of one data set correspond to the low points of a second data set, this is termed A. direct relationship B. positive relationship C. negative relationship D. inverse relationship |
Both C and D |
|
|
When the high and low scores of one set correspond respectively to the high and low scores of a second set this is called A. direct relationship B. positive relationship C. negative relationship D. inverse relationship |
A and B |
|
|
In a perfect direct relationship the Pearson r would have the value A. -1 B. 0 C. 1 D. 36 |
C |
|
|
In a perfect inverse relationship, the Pearson r will have the value A. -1 B. 0 C. 1 D. 36 |
A |
|
|
T or F As the relationships between two data sets become less and less perfect, the correlation coefficient gets closer and closer to 0. |
T |
|
|
T or F To get a percentage multiply the Pearson r by 100. |
F the Pearson r is not a proportion |
|
|
To get a proportion from a Pearson r one must A. square r B. find the square root of r C. invert r and find its square D. multiply r by 100 |
A |
|
|
Is it possible for a negative relationship to be strong? |
Yes! |
|
|
Which of the following options present the strongest relationship? A. 40 B. 0 C. -35 D. -60 |
D |
|
|
T or F An r of -90 is stronger than an r of 50. |
True - in determining strength take the absolute value. |
|
|
r^2 (r squared written in a program without subscripts) is known as A. the percentage value of the Pearson r B. an inverse relationship C. the t test D. coefficient of determination |
Both A and D |
|
|
A Pearson r of .70 is what percentage better than a Pearson r of -.65 A. 7% B. -5% C. 5% D. 15% |
A. - square both values, subtract the lesser value from the greater value |
|
|
The term “weak” is used to define a correlation coefficient with an absolute value of A. 0 B. .1 C. .3 D. .5 |
B |
|
|
To use the term “moderate” to define a correlation coefficient, the absolute value should be ____ or higher A. 0 B. .5 C. .3 D. .7 |
C |
|
|
The term “strong” as applied to correlation coefficients is in reference to an absolute value tat is ___ or higher A. 0 B. .5 C. .7 D. .9 |
B |
|
|
To understand the true relationship between two data sets one must examine both the A. determination coefficient and Pearson r B. Pearson r and histogram C. correlation coefficient and scatter plot D. t Test and percentage |
C |
|
|
Which should be examined first inferential statistical analysis or frequency distribution graphs? |
Frequency distribution graphs |
|
|
Hypothesis testing and parameter estimation are parts of A. descriptive statistics B. inferential statistics C. null hypothesis D. validity testing |
B |
|
|
The null hypothesis is designated by A. H. O. B. H. A. C. mu D. p
|
A |
|
|
An alternate hypothesis is designated by
A. H. O. B. H. A. C. mu D. p |
B |
|
|
Falsely rejecting the null hypothesis when it is true is a A. Type I error B. Type II error C. Type III error D. Type IV error |
A |
|
|
Failing to reject the null hypothesis when the hypothesis is false
A. Type I error B. Type II error C. Type III error D. Type IV error |
B |
|
|
T or F The values of t and df are of special interest to typical consumers of research. |
F |
|
|
If you read that t = 2.000, df = 20, p > .05 for the difference between two means. Has the null hypothesis been rejected? |
Nope |
|
|
If you read that t = 1.456, df = 34, p >.05 for the difference between two means. Is the null hypothesis rejected? |
No |
|
|
If you read t = 2.857, df = 30, p = < .01 for the difference between two means. Is the null hypothesis rejected? |
Yes |
|
|
If you read t = 2.857, df = 30, p = < .01 for the difference between two means. Is the difference between the two means statistically significant? |
Yes |
|
|
T or F When a researcher uses a large sample he/she is more likely to reject the null hypothesis than a researcher using a small sample |
T |
|
|
T or F When the size between the difference between means is small the researcher is more likely to reject the null hypothesis |
F |
|
|
If a researcher found that for a sample of 92 participants, r = .41, p<.001, would the researcher reject the null hypothesis? |
Yes |
|
|
If a researcher found that for a sample of 92 participants, r = .41, p<.001, is the study statistically significant? |
Yes |
|
|
List the three factors that lead to a low probability when t tests are conducted |
1. Sample size 2. Size of difference between means 3. Amount of variation in the population. |
|
|
List the three factors that lead to a low probability when t tests are conducted |
1. Sample size 2. Size of difference between means 3. Amount of variation in the population. |
|
|
The question of whether a difference is reliable in light of random errors is A. political acceptability B. main effect C. statistical significance D. crucial difference |
C |
|
|
T or F A small difference can be a statistically significant difference. |
T |
|
|
T or F A small difference can be a statistically significant difference. |
T |
|
|
T or F Statistical significance and practical significance are the same thing. |
F |
|
|
T or F A small difference can be a statistically significant difference. |
T |
|
|
T or F Statistical significance and practical significance are the same thing. |
F |
|
|
T or F Determining practical significance is a clear cut, mechanical measurement. |
F |
|
|
1. cost-benefit 2. crucial difference 3. client acceptability 4. public and political acceptability 5. ethical and legal implications All of these help determine ____ |
Practical significance |
|
|
T or F One way to standardize measurement is to express differences in terms of their standard deviation units. |
T |
|
|
Standard deviation units are expressed by the variable A. HO B. d C. m D. B |
B |
|
|
To determine the standard deviation unit one must A. Divide the difference between the two means by the size of the standard deviation unit. B. Multiply the difference between the two means by the size of the standard deviation unit. C. Divide the difference between the two means by the median. D. Multiply the difference between the two means by the median. |
A |
|
|
Effect size refers to A. the size of the difference between two groups (control and treatment) B. the size of the population that would be affected positively when using inferential statistics C. the comparison of the difference in outcomes between two groups D. the magnitude of a difference expressed on a standardized scale |
D |
|
|
Is it possible for an experiment with a smaller raw score difference to have a larger difference when the differences are expressed as d? |
Yes |
|
|
T or F Values of d greater than 1.30 are often found in social and behavioral research. |
F |
|
|
Should a test of statistical significance be conducted before or after computing d and interpreting its value using labels? |
Before |
|
|
T or F The terms d and effect size can be used interchangeably. |
F - effect size refers to any statistic that describes the size of a difference on a standardized metric. Cohen’s d is one of these statistics. |
|
|
If a researcher wants to determine which of two groups is superior on average a comparison using _____________ is usually preferred. A. Pearson r B. as C. M D. d |
D |
|
|
If there is one group of participants with two scores per participant and the goal is to determine the degree of relationship between the two sets of scores _____________ is used. A. Pearson r & r^2 B. as C. M D. d |
A |
|
|
A result value of .20 d for an experiment would be considered A. small B. medium C. large D. very large |
A |
|
|
A result value of 1.10 d for an experiment would be considered A. small B. medium C. large D. very large |
D |
|
|
A result value of .50 d for an experiment would be considered A. small B. medium C. large D. very large |
B |
|
|
A result value of .80 d for an experiment would be considered A. small B. medium C. large D. very large |
C |
|
|
T or F The effect size (d) is a measure of the experiments importance with large effect size correlating to high importance. |
F |
|
|
List the three steps for interpreting the difference between two means |
1. Determine if the difference is statistically significant. 2. Consider the value of d and the effect size 3. Consider the implications of the difference for validating research theories + the practical significance. |
|
|
Are there universally accepted standards for describing effect sizes? |
No but Cohen’s labels for values of d are often used |
|
|
Is it possible to have an effect size greater than 3.00? Why or why not? |
No - the standard deviation does not extend past 3.00 or -3.00 making those the maximum value of d |
|
|
T or F It is possible to have a negative value of d. |
T this happens rarely when a control groups mean is greater than the experimental groups mean. |
|
|
T or F Values of d greater than 1.30 are often found in social and behavioral research. |
F |
|
|
Should a test of statistical significance be conducted before or after computing d and interpreting its value using labels? |
Before |
|
|
T or F The terms d and effect size can be used interchangeably. |
F - effect size refers to any statistic that describes the size of a difference on a standardized metric. Cohen’s d is one of these statistics. |
|
|
If a researcher wants to determine which of two groups is superior on average a comparison using _____________ is usually preferred. A. Pearson r & r^2 B. as C. M D. d |
D |
|
|
If there is one group of participants with two scores per participant and the goal is to determine the degree of relationship between the two sets of scores _____________ is used. A. Pearson r & r^2 B. as C. M D. d |
A |
|
|
Is it possible to have a negative Pearson r^2 value? |
No - when a negative number is squared it becomes positive. |
|
|
A result value of .20 d for an experiment would be considered A. small B. medium C. large D. very large |
A |
|
|
A result value of 1.10 d for an experiment would be considered A. small B. medium C. large D. very large |
D |
|
|
A result value of .50 d for an experiment would be considered A. small B. medium C. large D. very large |
B |
|
|
A result value of .80 d for an experiment would be considered A. small B. medium C. large D. very large |
C |
|
|
T or F The effect size (d) is a measure of the experiments importance with large effect size correlating to high importance. |
F |
|
|
List the three steps for interpreting the difference between two means |
1. Determine if the difference is statistically significant. 2. Consider the value of d and the effect size 3. Consider the implications of the difference for validating research theories + the practical significance. |
|
|
Are there universally accepted standards for describing effect sizes? |
No but Cohen’s labels for values of d are often used |
|
|
Is it possible to have an effect size greater than 3.00? Why or why not? |
No - the standard deviation does not extend past 3.00 or -3.00 making those the maximum value of d |
|
|
T or F It is possible to have a negative value of d. |
T this happens rarely when a control groups mean is greater than the experimental groups mean. |
