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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

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

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.

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.

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.

Percentage Point Change

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

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.

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.

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.

Units

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

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.

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.

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.

Rate

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

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.

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.

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.

Risk

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

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.

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.

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.

Percent Change

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

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.

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.

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.

Odds *This question is required

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

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.

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.

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.

Rate of Change

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

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.

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.

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.

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

Space Cell

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.

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.

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

Prevalence

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.

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.

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

Bar Chart

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.

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.

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

Pie Chart

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.

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.

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

Frequency Distribution

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.

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.

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

Incidence

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.

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.

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

Histogram

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.

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.

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

Common measure of variability of a quantitative variable.

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

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

Extreme scores or observations that stand out in a distribution.

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

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

Mean

Common measure of variability of a quantitative variable.

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

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

Extreme scores or observations that stand out in a distribution.

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

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

Standard Deviation

Common measure of variability of a quantitative variable.

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

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

Extreme scores or observations that stand out in a distribution.

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

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

Common measure of variability of a quantitative variable.

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

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

Extreme scores or observations that stand out in a distribution.

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

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

Outliers

Common measure of variability of a quantitative variable.

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

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

Extreme scores or observations that stand out in a distribution.

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

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

Skewness

Common measure of variability of a quantitative variable.

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

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

Extreme scores or observations that stand out in a distribution.

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

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

Median

Common measure of variability of a quantitative variable.

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

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

Extreme scores or observations that stand out in a distribution.

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

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

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

Ratio of the risk of two groups.

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.

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)

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

Ratio of the risk of two groups.

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.

Method to describe the relationship between two categorical variables.

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

Quantile

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

Ratio of the risk of two groups.

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.

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

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

Ratio of the risk of two groups.

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.

Method to describe the relationship between two categorical variables.

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

Cross-Tabulation

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

Ratio of the risk of two groups.

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.

Method to describe the relationship between two categorical variables.

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

Relative Risk

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

Ratio of the risk of two groups.

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.

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)

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

Ratio of the risk of two groups.

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.

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

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.

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.

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

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

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.

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.

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.

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

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

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.

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.

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.

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

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

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.

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.

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.

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

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

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.

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.

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.

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

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

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.

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.

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.

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

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

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.

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.

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.

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

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

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.

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

Space Cell

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

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.

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

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

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

Constant (in Regression)

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

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.

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

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

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

R-Squared

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

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.

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

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

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

Residual *This question is required

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

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.

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

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

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

Effect Size

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

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.

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

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

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

Practical Significance

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

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.

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

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

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

Parameter

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

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.

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

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

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

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

Space Cell

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

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

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 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.

Statistical Inference

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

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

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 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.

Standard Error

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

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

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 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.

Confidence Interval *This question is required

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

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

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 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.

Level of Confidence

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

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

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 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.

Significance Test (or Hypothesis Test)

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

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

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 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.

Statistical Significance

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

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

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 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.

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.

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

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.

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

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

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.

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

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.

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

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

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.

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

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.

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

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

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.

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

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.

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

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

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.

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

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.

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

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

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.

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

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.

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

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

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.

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

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.

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

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

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

Space Cell

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.

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.

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

The acceptance of a false 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.

Type I Error

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.

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.

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

The acceptance of a false 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.

Type II Error

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.

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.

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

The acceptance of a false 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.

Power *This question is required

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.

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.

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

The acceptance of a false 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.

Minimal Detectable Effect

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.

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.

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

The acceptance of a false 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.

Multiple Comparison Correction

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.

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.

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

The acceptance of a false 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.

Sample Size Calculation

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.

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.

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

The acceptance of a false 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.

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.