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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.

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

How rapidly a variable changes.

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

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

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

Odds *This question is required

Change relative to the starting base, expressed as percentage.

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

How rapidly a variable changes.

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

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

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

Percentage Point Change

Change relative to the starting base, expressed as percentage.

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

How rapidly a variable changes.

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

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

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

Risk

Change relative to the starting base, expressed as percentage.

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

How rapidly a variable changes.

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

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

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

Rate

Change relative to the starting base, expressed as percentage.

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

How rapidly a variable changes.

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

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

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

Rate of Change

Change relative to the starting base, expressed as percentage.

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

How rapidly a variable changes.

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

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

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

Units

Change relative to the starting base, expressed as percentage.

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

How rapidly a variable changes.

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

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

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

Percent Change

Change relative to the starting base, expressed as percentage.

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

How rapidly a variable changes.

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

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

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

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

Space Cell

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

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

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

Pie Chart

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

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

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

Histogram

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

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

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

Bar Chart

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

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

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

Prevalence

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

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

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

Frequency Distribution

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

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

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

Incidence

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

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

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

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.

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.

Extreme scores or observations that stand out in a distribution.

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

Variance *This question is required

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.

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.

Extreme scores or observations that stand out in a distribution.

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

Skewness

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.

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.

Extreme scores or observations that stand out in a distribution.

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

Mean

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.

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.

Extreme scores or observations that stand out in a distribution.

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

Outliers

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.

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.

Extreme scores or observations that stand out in a distribution.

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

Median

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.

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.

Extreme scores or observations that stand out in a distribution.

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

Standard Deviation

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.

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.

Extreme scores or observations that stand out in a distribution.

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

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

Space Cell

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

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

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

Method to describe the relationship between two categorical variables.

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

Ratio of the risk of two groups.

Standardized Score (or z Score)

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

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

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

Method to describe the relationship between two categorical variables.

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

Ratio of the risk of two groups.

Quantile

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

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

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

Method to describe the relationship between two categorical variables.

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

Ratio of the risk of two groups.

Coefficient of Variation (COV) *This question is required

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

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

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

Method to describe the relationship between two categorical variables.

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

Ratio of the risk of two groups.

Cross-Tabulation

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

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

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

Method to describe the relationship between two categorical variables.

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

Ratio of the risk of two groups.

Relative Risk

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

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

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

Method to describe the relationship between two categorical variables.

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

Ratio of the risk of two groups.

Odds Ratio (OR)

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

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

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

Method to describe the relationship between two categorical variables.

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

Ratio of the risk of two groups.

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

Space Cell

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

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.

Scatterplot

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

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.

Correlation

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

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.

Pearson r *This question is required

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

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.

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.

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

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.

Simple Regression

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

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.

Coefficient of the Independent Variable (in Regression)

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

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.

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

Space Cell

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.

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

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.

Constant (in Regression)

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.

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

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.

R-Squared

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.

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

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.

Residual *This question is required

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.

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

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.

Effect Size

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.

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

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.

Practical Significance

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.

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

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.

Parameter

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.

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

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.

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.

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

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

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

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

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.

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

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

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

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

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.

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

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

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

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

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.

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

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

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

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

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.

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

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

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

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

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.

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

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

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

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

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.

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

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

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

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

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.

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.

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.

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.

Null Hypothesis

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

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.

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.

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.

Alternative Hypothesis

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

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.

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.

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.

Test Statistic *This question is required

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

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.

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.

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.

p Value

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

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.

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.

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.

Significance Level

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

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.

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.

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.

Chi-Square Test

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

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.

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.

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.

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

Space Cell

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.

The acceptance of a false null hypothesis.

The rejection of a true null hypothesis.

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.

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

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

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.

The acceptance of a false null hypothesis.

The rejection of a true null hypothesis.

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.

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

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

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.

The acceptance of a false null hypothesis.

The rejection of a true null hypothesis.

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.

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

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

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.

The acceptance of a false null hypothesis.

The rejection of a true null hypothesis.

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.

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

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

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.

The acceptance of a false null hypothesis.

The rejection of a true null hypothesis.

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.

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

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

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.

The acceptance of a false null hypothesis.

The rejection of a true null hypothesis.

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.

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

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

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.

The acceptance of a false null hypothesis.

The rejection of a true null hypothesis.

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.

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

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.