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Please answer each of the following questions to help you self-assess your understanding of "Chapter 8: Making Sense of the Numbers" (Remler & Van Ryzin, 2010)

1. (OPTIONAL) Your email address This question requires a valid email address.

2. Please Match the Term to Its Definition *This question is required.

Space Cell

The precise meaning of the numbers in quantitative variables - how many of what the numbers refer to. Also referred to as units of measurement.

The change of a variable measured in its own units when it is a percentage. Contrasted with percent change.

How rapidly a variable changes.

Share of a population with a particular characteristic, which is expressed relative to some base size population.

Share of a population with a particular condition or disease, which is expressed relative to some base size population.

Change relative to the starting base, expressed as percentage.

For an outcome that has only two possibilities, the ratio of one outcome (e.g., success) to the other possible outcome (e.g., failure).

Risk

The precise meaning of the numbers in quantitative variables - how many of what the numbers refer to. Also referred to as units of measurement.

The change of a variable measured in its own units when it is a percentage. Contrasted with percent change.

How rapidly a variable changes.

Share of a population with a particular characteristic, which is expressed relative to some base size population.

Share of a population with a particular condition or disease, which is expressed relative to some base size population.

Change relative to the starting base, expressed as percentage.

For an outcome that has only two possibilities, the ratio of one outcome (e.g., success) to the other possible outcome (e.g., failure).

Odds *This question is required

The precise meaning of the numbers in quantitative variables - how many of what the numbers refer to. Also referred to as units of measurement.

The change of a variable measured in its own units when it is a percentage. Contrasted with percent change.

How rapidly a variable changes.

Share of a population with a particular characteristic, which is expressed relative to some base size population.

Share of a population with a particular condition or disease, which is expressed relative to some base size population.

Change relative to the starting base, expressed as percentage.

For an outcome that has only two possibilities, the ratio of one outcome (e.g., success) to the other possible outcome (e.g., failure).

Percentage Point Change

The precise meaning of the numbers in quantitative variables - how many of what the numbers refer to. Also referred to as units of measurement.

The change of a variable measured in its own units when it is a percentage. Contrasted with percent change.

How rapidly a variable changes.

Share of a population with a particular characteristic, which is expressed relative to some base size population.

Share of a population with a particular condition or disease, which is expressed relative to some base size population.

Change relative to the starting base, expressed as percentage.

For an outcome that has only two possibilities, the ratio of one outcome (e.g., success) to the other possible outcome (e.g., failure).

Percent Change

The precise meaning of the numbers in quantitative variables - how many of what the numbers refer to. Also referred to as units of measurement.

The change of a variable measured in its own units when it is a percentage. Contrasted with percent change.

How rapidly a variable changes.

Share of a population with a particular characteristic, which is expressed relative to some base size population.

Share of a population with a particular condition or disease, which is expressed relative to some base size population.

Change relative to the starting base, expressed as percentage.

For an outcome that has only two possibilities, the ratio of one outcome (e.g., success) to the other possible outcome (e.g., failure).

Units

The precise meaning of the numbers in quantitative variables - how many of what the numbers refer to. Also referred to as units of measurement.

The change of a variable measured in its own units when it is a percentage. Contrasted with percent change.

How rapidly a variable changes.

Share of a population with a particular characteristic, which is expressed relative to some base size population.

Share of a population with a particular condition or disease, which is expressed relative to some base size population.

Change relative to the starting base, expressed as percentage.

For an outcome that has only two possibilities, the ratio of one outcome (e.g., success) to the other possible outcome (e.g., failure).

Rate of Change

The precise meaning of the numbers in quantitative variables - how many of what the numbers refer to. Also referred to as units of measurement.

The change of a variable measured in its own units when it is a percentage. Contrasted with percent change.

How rapidly a variable changes.

Share of a population with a particular characteristic, which is expressed relative to some base size population.

Share of a population with a particular condition or disease, which is expressed relative to some base size population.

Change relative to the starting base, expressed as percentage.

For an outcome that has only two possibilities, the ratio of one outcome (e.g., success) to the other possible outcome (e.g., failure).

Rate

The precise meaning of the numbers in quantitative variables - how many of what the numbers refer to. Also referred to as units of measurement.

The change of a variable measured in its own units when it is a percentage. Contrasted with percent change.

How rapidly a variable changes.

Share of a population with a particular characteristic, which is expressed relative to some base size population.

Share of a population with a particular condition or disease, which is expressed relative to some base size population.

Change relative to the starting base, expressed as percentage.

For an outcome that has only two possibilities, the ratio of one outcome (e.g., success) to the other possible outcome (e.g., failure).

3. Please Match the Term to Its Definition *This question is required.

Space Cell

A graph showing percentages among categories, shown as segments of a circle.

The number or share of the population that has a particular disease or condition.

A graph showing the distribution of a quantitative variable.

The rate at which new cases of a disease or condition appear in a population.

A graph for displaying categorical data with bars representing each category.

The distribution of a categorical variable showing the count or percentage in each category.

Frequency Distribution

A graph showing percentages among categories, shown as segments of a circle.

The number or share of the population that has a particular disease or condition.

A graph showing the distribution of a quantitative variable.

The rate at which new cases of a disease or condition appear in a population.

A graph for displaying categorical data with bars representing each category.

The distribution of a categorical variable showing the count or percentage in each category.

Prevalence

A graph showing percentages among categories, shown as segments of a circle.

The number or share of the population that has a particular disease or condition.

A graph showing the distribution of a quantitative variable.

The rate at which new cases of a disease or condition appear in a population.

A graph for displaying categorical data with bars representing each category.

The distribution of a categorical variable showing the count or percentage in each category.

Incidence

A graph showing percentages among categories, shown as segments of a circle.

The number or share of the population that has a particular disease or condition.

A graph showing the distribution of a quantitative variable.

The rate at which new cases of a disease or condition appear in a population.

A graph for displaying categorical data with bars representing each category.

The distribution of a categorical variable showing the count or percentage in each category.

Pie Chart

A graph showing percentages among categories, shown as segments of a circle.

The number or share of the population that has a particular disease or condition.

A graph showing the distribution of a quantitative variable.

The rate at which new cases of a disease or condition appear in a population.

A graph for displaying categorical data with bars representing each category.

The distribution of a categorical variable showing the count or percentage in each category.

Histogram

A graph showing percentages among categories, shown as segments of a circle.

The number or share of the population that has a particular disease or condition.

A graph showing the distribution of a quantitative variable.

The rate at which new cases of a disease or condition appear in a population.

A graph for displaying categorical data with bars representing each category.

The distribution of a categorical variable showing the count or percentage in each category.

Bar Chart

A graph showing percentages among categories, shown as segments of a circle.

The number or share of the population that has a particular disease or condition.

A graph showing the distribution of a quantitative variable.

The rate at which new cases of a disease or condition appear in a population.

A graph for displaying categorical data with bars representing each category.

The distribution of a categorical variable showing the count or percentage in each category.

4. Please Match the Term to Its Definition *This question is required.

Space Cell

Characteristic of a distribution that is not symmetrical and has one tail longer than the other.

Average of a quantitative variable - the sum of all observations divided by the number of observations.

Common measure of variability of a quantitative variable.

The value at the point that splits the distribution into two halves, the 50th percentile in the distribution of a quantitative variable.

A measure of spread of a quantitative variable, the square of the standard deviation.

Extreme scores or observations that stand out in a distribution.

Median

Characteristic of a distribution that is not symmetrical and has one tail longer than the other.

Average of a quantitative variable - the sum of all observations divided by the number of observations.

Common measure of variability of a quantitative variable.

The value at the point that splits the distribution into two halves, the 50th percentile in the distribution of a quantitative variable.

A measure of spread of a quantitative variable, the square of the standard deviation.

Extreme scores or observations that stand out in a distribution.

Mean

Characteristic of a distribution that is not symmetrical and has one tail longer than the other.

Average of a quantitative variable - the sum of all observations divided by the number of observations.

Common measure of variability of a quantitative variable.

The value at the point that splits the distribution into two halves, the 50th percentile in the distribution of a quantitative variable.

A measure of spread of a quantitative variable, the square of the standard deviation.

Extreme scores or observations that stand out in a distribution.

Outliers

Characteristic of a distribution that is not symmetrical and has one tail longer than the other.

Average of a quantitative variable - the sum of all observations divided by the number of observations.

Common measure of variability of a quantitative variable.

The value at the point that splits the distribution into two halves, the 50th percentile in the distribution of a quantitative variable.

A measure of spread of a quantitative variable, the square of the standard deviation.

Extreme scores or observations that stand out in a distribution.

Standard Deviation

Characteristic of a distribution that is not symmetrical and has one tail longer than the other.

Average of a quantitative variable - the sum of all observations divided by the number of observations.

Common measure of variability of a quantitative variable.

The value at the point that splits the distribution into two halves, the 50th percentile in the distribution of a quantitative variable.

A measure of spread of a quantitative variable, the square of the standard deviation.

Extreme scores or observations that stand out in a distribution.

Variance *This question is required

Characteristic of a distribution that is not symmetrical and has one tail longer than the other.

Average of a quantitative variable - the sum of all observations divided by the number of observations.

Common measure of variability of a quantitative variable.

The value at the point that splits the distribution into two halves, the 50th percentile in the distribution of a quantitative variable.

A measure of spread of a quantitative variable, the square of the standard deviation.

Extreme scores or observations that stand out in a distribution.

Skewness

Characteristic of a distribution that is not symmetrical and has one tail longer than the other.

Average of a quantitative variable - the sum of all observations divided by the number of observations.

Common measure of variability of a quantitative variable.

The value at the point that splits the distribution into two halves, the 50th percentile in the distribution of a quantitative variable.

A measure of spread of a quantitative variable, the square of the standard deviation.

Extreme scores or observations that stand out in a distribution.

5. Please Match the Term to Its Definition *This question is required.

Space Cell

Ratio of the risk of two groups.

A variable converted to standard deviation units and shifted to mean zero. Also known as a z score.

Ratio of the odds of an outcome for one group to the odds of the outcome for another group.

A measure of spread equal to the standard deviation divided by the mean.

Method to describe the relationship between two categorical variables.

Points taken at regular intervals (such as every quarter or tenth) in a distribution.

Standardized Score (or z Score)

Ratio of the risk of two groups.

A variable converted to standard deviation units and shifted to mean zero. Also known as a z score.

Ratio of the odds of an outcome for one group to the odds of the outcome for another group.

A measure of spread equal to the standard deviation divided by the mean.

Method to describe the relationship between two categorical variables.

Points taken at regular intervals (such as every quarter or tenth) in a distribution.

Quantile

Ratio of the risk of two groups.

A variable converted to standard deviation units and shifted to mean zero. Also known as a z score.

Ratio of the odds of an outcome for one group to the odds of the outcome for another group.

A measure of spread equal to the standard deviation divided by the mean.

Method to describe the relationship between two categorical variables.

Points taken at regular intervals (such as every quarter or tenth) in a distribution.

Coefficient of Variation (COV) *This question is required

Ratio of the risk of two groups.

A variable converted to standard deviation units and shifted to mean zero. Also known as a z score.

Ratio of the odds of an outcome for one group to the odds of the outcome for another group.

A measure of spread equal to the standard deviation divided by the mean.

Method to describe the relationship between two categorical variables.

Points taken at regular intervals (such as every quarter or tenth) in a distribution.

Cross-Tabulation

Ratio of the risk of two groups.

A variable converted to standard deviation units and shifted to mean zero. Also known as a z score.

Ratio of the odds of an outcome for one group to the odds of the outcome for another group.

A measure of spread equal to the standard deviation divided by the mean.

Method to describe the relationship between two categorical variables.

Points taken at regular intervals (such as every quarter or tenth) in a distribution.

Relative Risk

Ratio of the risk of two groups.

A variable converted to standard deviation units and shifted to mean zero. Also known as a z score.

Ratio of the odds of an outcome for one group to the odds of the outcome for another group.

A measure of spread equal to the standard deviation divided by the mean.

Method to describe the relationship between two categorical variables.

Points taken at regular intervals (such as every quarter or tenth) in a distribution.

Odds Ratio (OR)

Ratio of the risk of two groups.

A variable converted to standard deviation units and shifted to mean zero. Also known as a z score.

Ratio of the odds of an outcome for one group to the odds of the outcome for another group.

A measure of spread equal to the standard deviation divided by the mean.

Method to describe the relationship between two categorical variables.

Points taken at regular intervals (such as every quarter or tenth) in a distribution.

6. Please Match the Term to Its Definition *This question is required.

Space Cell

A measure of the strength and direction of a relationship between two variables.

The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. It is the most common measure of correlation. Also referred to as the correlation coefficient.

A graph illustrating the values two quantitative variables take on in data.

A best-fit straight line for describing how one quantitative variable - the independent variable - predicts another quantitative variable - the dependent variable.

The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. Also known as Pearson r or simply r.

The number that multiplies a given independent variable in a regression. Also known as the slope.

Scatterplot

A measure of the strength and direction of a relationship between two variables.

The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. It is the most common measure of correlation. Also referred to as the correlation coefficient.

A graph illustrating the values two quantitative variables take on in data.

A best-fit straight line for describing how one quantitative variable - the independent variable - predicts another quantitative variable - the dependent variable.

The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. Also known as Pearson r or simply r.

The number that multiplies a given independent variable in a regression. Also known as the slope.

Correlation

A measure of the strength and direction of a relationship between two variables.

The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. It is the most common measure of correlation. Also referred to as the correlation coefficient.

A graph illustrating the values two quantitative variables take on in data.

A best-fit straight line for describing how one quantitative variable - the independent variable - predicts another quantitative variable - the dependent variable.

The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. Also known as Pearson r or simply r.

The number that multiplies a given independent variable in a regression. Also known as the slope.

Pearson r *This question is required

A measure of the strength and direction of a relationship between two variables.

The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. It is the most common measure of correlation. Also referred to as the correlation coefficient.

A graph illustrating the values two quantitative variables take on in data.

A best-fit straight line for describing how one quantitative variable - the independent variable - predicts another quantitative variable - the dependent variable.

The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. Also known as Pearson r or simply r.

The number that multiplies a given independent variable in a regression. Also known as the slope.

Correlation Coefficient

A measure of the strength and direction of a relationship between two variables.

The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. It is the most common measure of correlation. Also referred to as the correlation coefficient.

A graph illustrating the values two quantitative variables take on in data.

A best-fit straight line for describing how one quantitative variable - the independent variable - predicts another quantitative variable - the dependent variable.

The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. Also known as Pearson r or simply r.

The number that multiplies a given independent variable in a regression. Also known as the slope.

Simple Regression

A measure of the strength and direction of a relationship between two variables.

The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. It is the most common measure of correlation. Also referred to as the correlation coefficient.

A graph illustrating the values two quantitative variables take on in data.

A best-fit straight line for describing how one quantitative variable - the independent variable - predicts another quantitative variable - the dependent variable.

The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. Also known as Pearson r or simply r.

The number that multiplies a given independent variable in a regression. Also known as the slope.

Coefficient of the Independent Variable (in Regression)

A measure of the strength and direction of a relationship between two variables.

The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. It is the most common measure of correlation. Also referred to as the correlation coefficient.

A graph illustrating the values two quantitative variables take on in data.

A best-fit straight line for describing how one quantitative variable - the independent variable - predicts another quantitative variable - the dependent variable.

The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. Also known as Pearson r or simply r.

The number that multiplies a given independent variable in a regression. Also known as the slope.

7. Please Match the Term to Its Definition *This question is required.

Space Cell

The extent to which an effect or relationship's magnitude (if true) would be important or relevant in the real world.

In a regression, the proportion of the variation in the dependent variable predicted by variation in the independent variables.

The characteristic or feature of a population that a research is trying to estimate.

The predicted value of the dependent variable when the independent variables are zero in a regression. Also known as the intercept.

The error in a regression - the difference between the actual value of the dependent variable and the predicted value.

A standardized way of measuring the effect of a treatment, usually the ratio of the effect or difference to the standard deviation.

Constant (in Regression)

The extent to which an effect or relationship's magnitude (if true) would be important or relevant in the real world.

In a regression, the proportion of the variation in the dependent variable predicted by variation in the independent variables.

The characteristic or feature of a population that a research is trying to estimate.

The predicted value of the dependent variable when the independent variables are zero in a regression. Also known as the intercept.

The error in a regression - the difference between the actual value of the dependent variable and the predicted value.

A standardized way of measuring the effect of a treatment, usually the ratio of the effect or difference to the standard deviation.

R-Squared

The extent to which an effect or relationship's magnitude (if true) would be important or relevant in the real world.

In a regression, the proportion of the variation in the dependent variable predicted by variation in the independent variables.

The characteristic or feature of a population that a research is trying to estimate.

The predicted value of the dependent variable when the independent variables are zero in a regression. Also known as the intercept.

The error in a regression - the difference between the actual value of the dependent variable and the predicted value.

A standardized way of measuring the effect of a treatment, usually the ratio of the effect or difference to the standard deviation.

Residual *This question is required

The extent to which an effect or relationship's magnitude (if true) would be important or relevant in the real world.

In a regression, the proportion of the variation in the dependent variable predicted by variation in the independent variables.

The characteristic or feature of a population that a research is trying to estimate.

The predicted value of the dependent variable when the independent variables are zero in a regression. Also known as the intercept.

The error in a regression - the difference between the actual value of the dependent variable and the predicted value.

A standardized way of measuring the effect of a treatment, usually the ratio of the effect or difference to the standard deviation.

Effect Size

The extent to which an effect or relationship's magnitude (if true) would be important or relevant in the real world.

In a regression, the proportion of the variation in the dependent variable predicted by variation in the independent variables.

The characteristic or feature of a population that a research is trying to estimate.

The predicted value of the dependent variable when the independent variables are zero in a regression. Also known as the intercept.

The error in a regression - the difference between the actual value of the dependent variable and the predicted value.

A standardized way of measuring the effect of a treatment, usually the ratio of the effect or difference to the standard deviation.

Practical Significance

The extent to which an effect or relationship's magnitude (if true) would be important or relevant in the real world.

In a regression, the proportion of the variation in the dependent variable predicted by variation in the independent variables.

The characteristic or feature of a population that a research is trying to estimate.

The predicted value of the dependent variable when the independent variables are zero in a regression. Also known as the intercept.

The error in a regression - the difference between the actual value of the dependent variable and the predicted value.

A standardized way of measuring the effect of a treatment, usually the ratio of the effect or difference to the standard deviation.

Parameter

The extent to which an effect or relationship's magnitude (if true) would be important or relevant in the real world.

In a regression, the proportion of the variation in the dependent variable predicted by variation in the independent variables.

The characteristic or feature of a population that a research is trying to estimate.

The predicted value of the dependent variable when the independent variables are zero in a regression. Also known as the intercept.

The error in a regression - the difference between the actual value of the dependent variable and the predicted value.

A standardized way of measuring the effect of a treatment, usually the ratio of the effect or difference to the standard deviation.

8. Please Match the Term to Its Definition *This question is required.

Space Cell

A range of values in which we have a defined level of confidence (e.g. 95%) that the true value of the statistic being estimated lies.

A test to see if a result is unlikely due to chance. Used to test whether groups are really different.

The extent to which a difference or a relationship exists, judged against the likelihood that it would happen just by chance alone.

The precision of the estimate - how good a job we expect it to do, on average.

Formal procedure that uses facts about the sampling distribution of statistics from a sample to infer the unknown parameters of a population.

The area - usually 95% - of the sampling distribution that is the basis for a confidence interval.

Statistical Inference

A range of values in which we have a defined level of confidence (e.g. 95%) that the true value of the statistic being estimated lies.

A test to see if a result is unlikely due to chance. Used to test whether groups are really different.

The extent to which a difference or a relationship exists, judged against the likelihood that it would happen just by chance alone.

The precision of the estimate - how good a job we expect it to do, on average.

Formal procedure that uses facts about the sampling distribution of statistics from a sample to infer the unknown parameters of a population.

The area - usually 95% - of the sampling distribution that is the basis for a confidence interval.

Standard Error

A range of values in which we have a defined level of confidence (e.g. 95%) that the true value of the statistic being estimated lies.

A test to see if a result is unlikely due to chance. Used to test whether groups are really different.

The extent to which a difference or a relationship exists, judged against the likelihood that it would happen just by chance alone.

The precision of the estimate - how good a job we expect it to do, on average.

Formal procedure that uses facts about the sampling distribution of statistics from a sample to infer the unknown parameters of a population.

The area - usually 95% - of the sampling distribution that is the basis for a confidence interval.

Confidence Interval *This question is required

A range of values in which we have a defined level of confidence (e.g. 95%) that the true value of the statistic being estimated lies.

A test to see if a result is unlikely due to chance. Used to test whether groups are really different.

The extent to which a difference or a relationship exists, judged against the likelihood that it would happen just by chance alone.

The precision of the estimate - how good a job we expect it to do, on average.

Formal procedure that uses facts about the sampling distribution of statistics from a sample to infer the unknown parameters of a population.

The area - usually 95% - of the sampling distribution that is the basis for a confidence interval.

Level of Confidence

A range of values in which we have a defined level of confidence (e.g. 95%) that the true value of the statistic being estimated lies.

A test to see if a result is unlikely due to chance. Used to test whether groups are really different.

The extent to which a difference or a relationship exists, judged against the likelihood that it would happen just by chance alone.

The precision of the estimate - how good a job we expect it to do, on average.

Formal procedure that uses facts about the sampling distribution of statistics from a sample to infer the unknown parameters of a population.

The area - usually 95% - of the sampling distribution that is the basis for a confidence interval.

Significance Test (or Hypothesis Test)

A range of values in which we have a defined level of confidence (e.g. 95%) that the true value of the statistic being estimated lies.

A test to see if a result is unlikely due to chance. Used to test whether groups are really different.

The extent to which a difference or a relationship exists, judged against the likelihood that it would happen just by chance alone.

The precision of the estimate - how good a job we expect it to do, on average.

Formal procedure that uses facts about the sampling distribution of statistics from a sample to infer the unknown parameters of a population.

The area - usually 95% - of the sampling distribution that is the basis for a confidence interval.

Statistical Significance

A range of values in which we have a defined level of confidence (e.g. 95%) that the true value of the statistic being estimated lies.

A test to see if a result is unlikely due to chance. Used to test whether groups are really different.

The extent to which a difference or a relationship exists, judged against the likelihood that it would happen just by chance alone.

The precision of the estimate - how good a job we expect it to do, on average.

Formal procedure that uses facts about the sampling distribution of statistics from a sample to infer the unknown parameters of a population.

The area - usually 95% - of the sampling distribution that is the basis for a confidence interval.

9. Please Match the Term to Its Definition *This question is required.

Space Cell

In hypothesis testing, the hypothesis that is directly tested, typically resulting in no difference or no effect.

Statistical test most commonly employed to see if two categorical variables are related.

A statistic used for significance testing (or hypothesis testing), calculated using data.

The standard against which the p value is compared to determine statistical significance: If the p value is less than the significance level, the result is deemed statistically significant.

The probability of observing our sample estimate (or one more extreme) if the null hypothesis about the population is true.

A negation of the null hypothesis; usually the hypothesis researchers would like to test but cannot do so directly.

Null Hypothesis

In hypothesis testing, the hypothesis that is directly tested, typically resulting in no difference or no effect.

Statistical test most commonly employed to see if two categorical variables are related.

A statistic used for significance testing (or hypothesis testing), calculated using data.

The standard against which the p value is compared to determine statistical significance: If the p value is less than the significance level, the result is deemed statistically significant.

The probability of observing our sample estimate (or one more extreme) if the null hypothesis about the population is true.

A negation of the null hypothesis; usually the hypothesis researchers would like to test but cannot do so directly.

Alternative Hypothesis

In hypothesis testing, the hypothesis that is directly tested, typically resulting in no difference or no effect.

Statistical test most commonly employed to see if two categorical variables are related.

A statistic used for significance testing (or hypothesis testing), calculated using data.

The standard against which the p value is compared to determine statistical significance: If the p value is less than the significance level, the result is deemed statistically significant.

The probability of observing our sample estimate (or one more extreme) if the null hypothesis about the population is true.

A negation of the null hypothesis; usually the hypothesis researchers would like to test but cannot do so directly.

Test Statistic *This question is required

In hypothesis testing, the hypothesis that is directly tested, typically resulting in no difference or no effect.

Statistical test most commonly employed to see if two categorical variables are related.

A statistic used for significance testing (or hypothesis testing), calculated using data.

The standard against which the p value is compared to determine statistical significance: If the p value is less than the significance level, the result is deemed statistically significant.

The probability of observing our sample estimate (or one more extreme) if the null hypothesis about the population is true.

A negation of the null hypothesis; usually the hypothesis researchers would like to test but cannot do so directly.

p Value

In hypothesis testing, the hypothesis that is directly tested, typically resulting in no difference or no effect.

Statistical test most commonly employed to see if two categorical variables are related.

A statistic used for significance testing (or hypothesis testing), calculated using data.

The standard against which the p value is compared to determine statistical significance: If the p value is less than the significance level, the result is deemed statistically significant.

The probability of observing our sample estimate (or one more extreme) if the null hypothesis about the population is true.

A negation of the null hypothesis; usually the hypothesis researchers would like to test but cannot do so directly.

Significance Level

In hypothesis testing, the hypothesis that is directly tested, typically resulting in no difference or no effect.

Statistical test most commonly employed to see if two categorical variables are related.

A statistic used for significance testing (or hypothesis testing), calculated using data.

The standard against which the p value is compared to determine statistical significance: If the p value is less than the significance level, the result is deemed statistically significant.

The probability of observing our sample estimate (or one more extreme) if the null hypothesis about the population is true.

A negation of the null hypothesis; usually the hypothesis researchers would like to test but cannot do so directly.

Chi-Square Test

In hypothesis testing, the hypothesis that is directly tested, typically resulting in no difference or no effect.

Statistical test most commonly employed to see if two categorical variables are related.

A statistic used for significance testing (or hypothesis testing), calculated using data.

The standard against which the p value is compared to determine statistical significance: If the p value is less than the significance level, the result is deemed statistically significant.

The probability of observing our sample estimate (or one more extreme) if the null hypothesis about the population is true.

A negation of the null hypothesis; usually the hypothesis researchers would like to test but cannot do so directly.

10. Copy of Please Match the Term to Its Definition *This question is required.

Space Cell

The acceptance of a false null hypothesis.

The smallest effect that would still have statistical significance in a study with a particular sample size and design, often chosen to perform sample size calculations.

A calculation done before a study or survey to determine the sample size needed to get a certain level of precision or to be able to detect certain differences.

Correction applied to a single statistical significance measure, when it is one of many statistical tests, because one of the many tests could be significant by chance.

In statistics, the ability to recognize that the null hypothesis is false.

The rejection of a true null hypothesis.

Type I Error

The acceptance of a false null hypothesis.

The smallest effect that would still have statistical significance in a study with a particular sample size and design, often chosen to perform sample size calculations.

A calculation done before a study or survey to determine the sample size needed to get a certain level of precision or to be able to detect certain differences.

Correction applied to a single statistical significance measure, when it is one of many statistical tests, because one of the many tests could be significant by chance.

In statistics, the ability to recognize that the null hypothesis is false.

The rejection of a true null hypothesis.

Type II Error

The acceptance of a false null hypothesis.

The smallest effect that would still have statistical significance in a study with a particular sample size and design, often chosen to perform sample size calculations.

A calculation done before a study or survey to determine the sample size needed to get a certain level of precision or to be able to detect certain differences.

Correction applied to a single statistical significance measure, when it is one of many statistical tests, because one of the many tests could be significant by chance.

In statistics, the ability to recognize that the null hypothesis is false.

The rejection of a true null hypothesis.

Power *This question is required

The acceptance of a false null hypothesis.

The smallest effect that would still have statistical significance in a study with a particular sample size and design, often chosen to perform sample size calculations.

A calculation done before a study or survey to determine the sample size needed to get a certain level of precision or to be able to detect certain differences.

Correction applied to a single statistical significance measure, when it is one of many statistical tests, because one of the many tests could be significant by chance.

In statistics, the ability to recognize that the null hypothesis is false.

The rejection of a true null hypothesis.

Minimal Detectable Effect

The acceptance of a false null hypothesis.

The smallest effect that would still have statistical significance in a study with a particular sample size and design, often chosen to perform sample size calculations.

A calculation done before a study or survey to determine the sample size needed to get a certain level of precision or to be able to detect certain differences.

Correction applied to a single statistical significance measure, when it is one of many statistical tests, because one of the many tests could be significant by chance.

In statistics, the ability to recognize that the null hypothesis is false.

The rejection of a true null hypothesis.

Multiple Comparison Correction

The acceptance of a false null hypothesis.

The smallest effect that would still have statistical significance in a study with a particular sample size and design, often chosen to perform sample size calculations.

A calculation done before a study or survey to determine the sample size needed to get a certain level of precision or to be able to detect certain differences.

Correction applied to a single statistical significance measure, when it is one of many statistical tests, because one of the many tests could be significant by chance.

In statistics, the ability to recognize that the null hypothesis is false.

The rejection of a true null hypothesis.

Sample Size Calculation

The acceptance of a false null hypothesis.

The smallest effect that would still have statistical significance in a study with a particular sample size and design, often chosen to perform sample size calculations.

A calculation done before a study or survey to determine the sample size needed to get a certain level of precision or to be able to detect certain differences.

Correction applied to a single statistical significance measure, when it is one of many statistical tests, because one of the many tests could be significant by chance.

In statistics, the ability to recognize that the null hypothesis is false.

The rejection of a true null hypothesis.

11. Which would you NOT use to show how many people live in each of four different regions of the United States (Midwest, North, South, West)? *This question is required.

Histogram

Bar Chart

Pie Chart

Frequency Distribution

12. In a small rural hamlet, everyone has a high school diploma, but one resident has a masterâ€™s degree. How would you refer to this one case? *This question is required.

Mean

Median

Mode

Outlier

13. Which of the following would you use to show the relationship between age (in years) and income (in dollars)? *This question is required.

Histogram

Odds Ratio

Coefficient of Variation

Scatter Plot

14. Which is not used with quantitative or continuous variables? *This question is required.

Histogram

Cross Tabulation

Simple Regression

Scatter Plot

15. The null hypothesis is rejected when *This question is required.

The significance level is high

The confidence level is low

The p value is low

The test statistic is low

16. A study concluded that musical ability is not associated with analytical ability when in fact there is a relationship. This mistake is called *This question is required.