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# R & vanR Chapter 8 Quiz

## Page 1 Questions

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2. Please Match the Term to Its Definition *This question is required.
Share of a population with a particular characteristic, which is expressed relative to some base size population. How rapidly a variable changes. 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). Share of a population with a particular condition or disease, 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. Change relative to the starting base, expressed as percentage. Space Cell Share of a population with a particular characteristic, which is expressed relative to some base size population. How rapidly a variable changes. 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). Share of a population with a particular condition or disease, 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. 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. 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). Share of a population with a particular condition or disease, 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. 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. 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). Share of a population with a particular condition or disease, 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. 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. 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). Share of a population with a particular condition or disease, 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. 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. 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). Share of a population with a particular condition or disease, 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. 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. 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). Share of a population with a particular condition or disease, 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. 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. 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). Share of a population with a particular condition or disease, 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. Change relative to the starting base, expressed as percentage.
3. Please Match the Term to Its Definition *This question is required.
The rate at which new cases of a disease or condition appear in a population. A graph for displaying categorical data with bars representing each category. The number or share of the population that has a particular disease or condition. The distribution of a categorical variable showing the count or percentage in each category. A graph showing the distribution of a quantitative variable. A graph showing percentages among categories, shown as segments of a circle. Space Cell The rate at which new cases of a disease or condition appear in a population. A graph for displaying categorical data with bars representing each category. The number or share of the population that has a particular disease or condition. The distribution of a categorical variable showing the count or percentage in each category. A graph showing the distribution of a quantitative variable. A graph showing percentages among categories, shown as segments of a circle. The rate at which new cases of a disease or condition appear in a population. A graph for displaying categorical data with bars representing each category. The number or share of the population that has a particular disease or condition. The distribution of a categorical variable showing the count or percentage in each category. A graph showing the distribution of a quantitative variable. A graph showing percentages among categories, shown as segments of a circle. The rate at which new cases of a disease or condition appear in a population. A graph for displaying categorical data with bars representing each category. The number or share of the population that has a particular disease or condition. The distribution of a categorical variable showing the count or percentage in each category. A graph showing the distribution of a quantitative variable. A graph showing percentages among categories, shown as segments of a circle. The rate at which new cases of a disease or condition appear in a population. A graph for displaying categorical data with bars representing each category. The number or share of the population that has a particular disease or condition. The distribution of a categorical variable showing the count or percentage in each category. A graph showing the distribution of a quantitative variable. A graph showing percentages among categories, shown as segments of a circle. The rate at which new cases of a disease or condition appear in a population. A graph for displaying categorical data with bars representing each category. The number or share of the population that has a particular disease or condition. The distribution of a categorical variable showing the count or percentage in each category. A graph showing the distribution of a quantitative variable. A graph showing percentages among categories, shown as segments of a circle. The rate at which new cases of a disease or condition appear in a population. A graph for displaying categorical data with bars representing each category. The number or share of the population that has a particular disease or condition. The distribution of a categorical variable showing the count or percentage in each category. A graph showing the distribution of a quantitative variable. A graph showing percentages among categories, shown as segments of a circle.
4. Please Match the Term to Its Definition *This question is required.
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. Extreme scores or observations that stand out in a distribution. 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. Space Cell 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. Extreme scores or observations that stand out in a distribution. 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. The value at the point that splits the distribution into two halves, the 50th percentile in the distribution of a quantitative variable. Extreme scores or observations that stand out in a distribution. 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. The value at the point that splits the distribution into two halves, the 50th percentile in the distribution of a quantitative variable. Extreme scores or observations that stand out in a distribution. 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. The value at the point that splits the distribution into two halves, the 50th percentile in the distribution of a quantitative variable. Extreme scores or observations that stand out in a distribution. 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. The value at the point that splits the distribution into two halves, the 50th percentile in the distribution of a quantitative variable. Extreme scores or observations that stand out in a distribution. 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. The value at the point that splits the distribution into two halves, the 50th percentile in the distribution of a quantitative variable. Extreme scores or observations that stand out in a distribution. 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.
5. Please Match the Term to Its Definition *This question is required.
Method to describe the relationship between two categorical variables. 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. Points taken at regular intervals (such as every quarter or tenth) in a distribution. A variable converted to standard deviation units and shifted to mean zero. Also known as a z score. A measure of spread equal to the standard deviation divided by the mean. Space Cell Method to describe the relationship between two categorical variables. 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. Points taken at regular intervals (such as every quarter or tenth) in a distribution. A variable converted to standard deviation units and shifted to mean zero. Also known as a z score. A measure of spread equal to the standard deviation divided by the mean. Method to describe the relationship between two categorical variables. 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. Points taken at regular intervals (such as every quarter or tenth) in a distribution. A variable converted to standard deviation units and shifted to mean zero. Also known as a z score. A measure of spread equal to the standard deviation divided by the mean. Method to describe the relationship between two categorical variables. 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. Points taken at regular intervals (such as every quarter or tenth) in a distribution. A variable converted to standard deviation units and shifted to mean zero. Also known as a z score. A measure of spread equal to the standard deviation divided by the mean. Method to describe the relationship between two categorical variables. 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. Points taken at regular intervals (such as every quarter or tenth) in a distribution. A variable converted to standard deviation units and shifted to mean zero. Also known as a z score. A measure of spread equal to the standard deviation divided by the mean. Method to describe the relationship between two categorical variables. 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. Points taken at regular intervals (such as every quarter or tenth) in a distribution. A variable converted to standard deviation units and shifted to mean zero. Also known as a z score. A measure of spread equal to the standard deviation divided by the mean. Method to describe the relationship between two categorical variables. 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. Points taken at regular intervals (such as every quarter or tenth) in a distribution. A variable converted to standard deviation units and shifted to mean zero. Also known as a z score. 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.
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. A measure of the strength and direction of a relationship between two variables. The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. 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. Space Cell 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. A measure of the strength and direction of a relationship between two variables. The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. 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. 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. A measure of the strength and direction of a relationship between two variables. The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. 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. 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. A measure of the strength and direction of a relationship between two variables. The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. 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. 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. A measure of the strength and direction of a relationship between two variables. The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. 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. 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. A measure of the strength and direction of a relationship between two variables. The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. 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. 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. A measure of the strength and direction of a relationship between two variables. The expected standard deviation change in one variable if the other variable changes by 1 standard deviation. 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.
7. Please Match the Term to Its Definition *This question is required.
A standardized way of measuring the effect of a treatment, usually the ratio of the effect or difference to the standard deviation. In a regression, the proportion of the variation in the dependent variable predicted by variation in the independent variables. The predicted value of the dependent variable when the independent variables are zero in a regression. Also known as the intercept. The characteristic or feature of a population that a research is trying to estimate. 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. Space Cell A standardized way of measuring the effect of a treatment, usually the ratio of the effect or difference to the standard deviation. In a regression, the proportion of the variation in the dependent variable predicted by variation in the independent variables. The predicted value of the dependent variable when the independent variables are zero in a regression. Also known as the intercept. The characteristic or feature of a population that a research is trying to estimate. 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. A standardized way of measuring the effect of a treatment, usually the ratio of the effect or difference to the standard deviation. In a regression, the proportion of the variation in the dependent variable predicted by variation in the independent variables. The predicted value of the dependent variable when the independent variables are zero in a regression. Also known as the intercept. The characteristic or feature of a population that a research is trying to estimate. 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. A standardized way of measuring the effect of a treatment, usually the ratio of the effect or difference to the standard deviation. In a regression, the proportion of the variation in the dependent variable predicted by variation in the independent variables. The predicted value of the dependent variable when the independent variables are zero in a regression. Also known as the intercept. The characteristic or feature of a population that a research is trying to estimate. 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. A standardized way of measuring the effect of a treatment, usually the ratio of the effect or difference to the standard deviation. In a regression, the proportion of the variation in the dependent variable predicted by variation in the independent variables. The predicted value of the dependent variable when the independent variables are zero in a regression. Also known as the intercept. The characteristic or feature of a population that a research is trying to estimate. 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. A standardized way of measuring the effect of a treatment, usually the ratio of the effect or difference to the standard deviation. In a regression, the proportion of the variation in the dependent variable predicted by variation in the independent variables. The predicted value of the dependent variable when the independent variables are zero in a regression. Also known as the intercept. The characteristic or feature of a population that a research is trying to estimate. 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. A standardized way of measuring the effect of a treatment, usually the ratio of the effect or difference to the standard deviation. In a regression, the proportion of the variation in the dependent variable predicted by variation in the independent variables. The predicted value of the dependent variable when the independent variables are zero in a regression. Also known as the intercept. The characteristic or feature of a population that a research is trying to estimate. 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.
Formal procedure that uses facts about the sampling distribution of statistics from a sample to infer the unknown parameters of a population. The precision of the estimate - how good a job we expect it to do, on average. 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 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. A test to see if a result is unlikely due to chance. Used to test whether groups are really different. Space Cell Formal procedure that uses facts about the sampling distribution of statistics from a sample to infer the unknown parameters of a population. The precision of the estimate - how good a job we expect it to do, on average. 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 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. A test to see if a result is unlikely due to chance. Used to test whether groups are really different. Formal procedure that uses facts about the sampling distribution of statistics from a sample to infer the unknown parameters of a population. The precision of the estimate - how good a job we expect it to do, on average. 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 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. A test to see if a result is unlikely due to chance. Used to test whether groups are really different. Formal procedure that uses facts about the sampling distribution of statistics from a sample to infer the unknown parameters of a population. The precision of the estimate - how good a job we expect it to do, on average. 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 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. A test to see if a result is unlikely due to chance. Used to test whether groups are really different. Formal procedure that uses facts about the sampling distribution of statistics from a sample to infer the unknown parameters of a population. The precision of the estimate - how good a job we expect it to do, on average. 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 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. A test to see if a result is unlikely due to chance. Used to test whether groups are really different. Formal procedure that uses facts about the sampling distribution of statistics from a sample to infer the unknown parameters of a population. The precision of the estimate - how good a job we expect it to do, on average. 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 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. A test to see if a result is unlikely due to chance. Used to test whether groups are really different. Formal procedure that uses facts about the sampling distribution of statistics from a sample to infer the unknown parameters of a population. The precision of the estimate - how good a job we expect it to do, on average. 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 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. A test to see if a result is unlikely due to chance. Used to test whether groups are really different.
9. Please Match the Term to Its Definition *This question is required.
In hypothesis testing, the hypothesis that is directly tested, typically resulting in no difference or no effect. 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. 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. Statistical test most commonly employed to see if two categorical variables are related. Space Cell In hypothesis testing, the hypothesis that is directly tested, typically resulting in no difference or no effect. 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. 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. Statistical test most commonly employed to see if two categorical variables are related. In hypothesis testing, the hypothesis that is directly tested, typically resulting in no difference or no effect. 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. 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. Statistical test most commonly employed to see if two categorical variables are related. In hypothesis testing, the hypothesis that is directly tested, typically resulting in no difference or no effect. 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. 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. Statistical test most commonly employed to see if two categorical variables are related. In hypothesis testing, the hypothesis that is directly tested, typically resulting in no difference or no effect. 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. 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. Statistical test most commonly employed to see if two categorical variables are related. In hypothesis testing, the hypothesis that is directly tested, typically resulting in no difference or no effect. 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. 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. Statistical test most commonly employed to see if two categorical variables are related. In hypothesis testing, the hypothesis that is directly tested, typically resulting in no difference or no effect. 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. 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. 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.
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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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.
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.
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.
13. Which of the following would you use to show the relationship between age (in years) and income (in dollars)? *This question is required.
14. Which is not used with quantitative or continuous variables? *This question is required.
15. The null hypothesis is rejected when *This question is required.
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.