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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

Change relative to the starting base, expressed as percentage.

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

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

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

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

How rapidly a variable changes.

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

Risk

Change relative to the starting base, expressed as percentage.

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

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

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

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

How rapidly a variable changes.

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

Percentage Point Change

Change relative to the starting base, expressed as percentage.

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

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

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

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

How rapidly a variable changes.

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

Units

Change relative to the starting base, expressed as percentage.

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

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

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

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

How rapidly a variable changes.

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

Percent Change

Change relative to the starting base, expressed as percentage.

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

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

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

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

How rapidly a variable changes.

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

Rate of Change

Change relative to the starting base, expressed as percentage.

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

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

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

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

How rapidly a variable changes.

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

Rate

Change relative to the starting base, expressed as percentage.

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

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

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

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

How rapidly a variable changes.

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

Odds *This question is required

Change relative to the starting base, expressed as percentage.

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

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

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

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

How rapidly a variable changes.

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

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

Space Cell

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

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

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

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

Pie Chart

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

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

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

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

Incidence

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

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

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

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

Prevalence

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

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

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

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

Frequency Distribution

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

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

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

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

Bar Chart

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

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

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

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

Histogram

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

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

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

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

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

Space Cell

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

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

Common measure of variability of a quantitative variable.

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

Extreme scores or observations that stand out in a distribution.

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

Mean

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

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

Common measure of variability of a quantitative variable.

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

Extreme scores or observations that stand out in a distribution.

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

Skewness

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

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

Common measure of variability of a quantitative variable.

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

Extreme scores or observations that stand out in a distribution.

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

Median

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

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

Common measure of variability of a quantitative variable.

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

Extreme scores or observations that stand out in a distribution.

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

Outliers

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

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

Common measure of variability of a quantitative variable.

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

Extreme scores or observations that stand out in a distribution.

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

Variance *This question is required

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

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

Common measure of variability of a quantitative variable.

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

Extreme scores or observations that stand out in a distribution.

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

Standard Deviation

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

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

Common measure of variability of a quantitative variable.

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

Extreme scores or observations that stand out in a distribution.

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

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

Space Cell

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

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

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

Ratio of the risk of two groups.

Method to describe the relationship between two categorical variables.

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

Standardized Score (or z Score)

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

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

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

Ratio of the risk of two groups.

Method to describe the relationship between two categorical variables.

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

Quantile

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

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

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

Ratio of the risk of two groups.

Method to describe the relationship between two categorical variables.

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

Coefficient of Variation (COV) *This question is required

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

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

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

Ratio of the risk of two groups.

Method to describe the relationship between two categorical variables.

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

Cross-Tabulation

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

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

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

Ratio of the risk of two groups.

Method to describe the relationship between two categorical variables.

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

Relative Risk

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

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

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

Ratio of the risk of two groups.

Method to describe the relationship between two categorical variables.

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

Odds Ratio (OR)

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

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

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

Ratio of the risk of two groups.

Method to describe the relationship between two categorical variables.

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

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

Space Cell

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

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

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

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

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.

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.

Scatterplot

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

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

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

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

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.

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.

Correlation

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

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

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

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

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.

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.

Pearson r *This question is required

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

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

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

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

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.

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.

Correlation Coefficient

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

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

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

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

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.

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.

Simple Regression

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

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

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

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

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.

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.

Coefficient of the Independent Variable (in Regression)

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

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

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

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

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.

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.

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

Space Cell

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Parameter

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

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

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

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

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

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

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

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

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

Statistical Inference

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

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

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

Standard Error

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

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

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

Confidence Interval *This question is required

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

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

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

Level of Confidence

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

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

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

Significance Test (or Hypothesis Test)

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

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

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

Statistical Significance

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

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

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

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

Space Cell

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

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

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.

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

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.

Null Hypothesis

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

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

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.

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

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.

Alternative Hypothesis

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

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

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.

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

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.

Test Statistic *This question is required

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

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

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.

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

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.

p Value

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

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

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.

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

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.

Significance Level

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

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

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.

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

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.

Chi-Square Test

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

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

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.

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

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.

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

Space Cell

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.

The acceptance of a false null hypothesis.

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

The rejection of a true 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.

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.

Type I Error

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.

The acceptance of a false null hypothesis.

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

The rejection of a true 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.

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.

Type II Error

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.

The acceptance of a false null hypothesis.

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

The rejection of a true 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.

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.

Power *This question is required

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.

The acceptance of a false null hypothesis.

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

The rejection of a true 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.

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.

Minimal Detectable Effect

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.

The acceptance of a false null hypothesis.

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

The rejection of a true 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.

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.

Multiple Comparison Correction

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.

The acceptance of a false null hypothesis.

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

The rejection of a true 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.

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.

Sample Size Calculation

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.

The acceptance of a false null hypothesis.

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

The rejection of a true 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.

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.

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.