MAT

b33d_e22d

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

null hypothesis, H₀ we want to fail to support this hypothesis in our chi-square (χ²) test alternative hypothesis, H_a for this hypothesis, the expected values are often impossible to calculate p-value the probability of getting a result that is either the same or more extreme than the actual observations critical value the boundary of how extreme a test statistic we need to support the null hypothesis, H₀ degrees of freedom in most chi-square (χ²) tests, it is the number of observed categories (rows in the table) minus one MAT

b33d_d6fc

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

null hypothesis, H₀ this hypothesis makes it easier to calculate the expected values alternative hypothesis, H_a if the null hypothesis, H₀ cannot be supported , then this opposing hypothesis is supported p-value a small value suggests the observed data are NOT consistent with the null hypothesis, H₀ critical value found in a table for a given degrees of freedom and level of significance, α degrees of freedom represents how many independent values can vary after constraints are applied MAT

b33d_22b8

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

p-value a small value suggests the observed data are NOT consistent with the null hypothesis, H₀ level of significance, α standard cutoff probability used to determine statistic significance critical value the cutoff used to compare against the observed chi-square (χ²) test statistic null hypothesis, H₀ we want to fail to support this hypothesis in our chi-square (χ²) test degrees of freedom represents how many independent values can vary after constraints are applied MAT

b33d_fda6

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

chi-square (χ²) test statistic if this value is large, then there is stronger evidence to support the alternative hypothesis, H_a null hypothesis, H₀ we attempt to find evidence against this hypothesis in our chi-square (χ²) test p-value the smaller this number, the bigger the chi-square (χ²) test statistic alternative hypothesis, H_a this hypothesis represents a deviation or bias from the expected pattern level of significance, α a fixed probability cutoff whether the null hypothesis, H₀ can be or cannot be supported MAT

b33d_fa76

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

level of significance, α biologists use a probability of 0.05 (5%) for this value null hypothesis, H₀ we attempt to find evidence against this hypothesis in our chi-square (χ²) test critical value the boundary of how extreme a test statistic we need to support the null hypothesis, H₀ chi-square (χ²) test statistic if this value is large, then there is stronger evidence to support the alternative hypothesis, H_a degrees of freedom usually one less than the number of observed categories (rows in the table) MAT

b33d_27f1

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

level of significance, α the fixed probability for whether or NOT the null hypothesis, H₀ can be supported null hypothesis, H₀ we want to fail to support this hypothesis in our chi-square (χ²) test critical value found in a table for a given degrees of freedom and level of significance, α chi-square (χ²) test statistic the bigger this number, the smaller the p-value alternative hypothesis, H_a if the null hypothesis, H₀ cannot be supported , then this opposing hypothesis is supported MAT

b33d_b860

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

alternative hypothesis, H_a for this hypothesis, the expected values are often impossible to calculate level of significance, α a fixed probability cutoff whether the null hypothesis, H₀ can be or cannot be supported p-value the smaller this number, the bigger the chi-square (χ²) test statistic degrees of freedom this number determines which row of the chi-square (χ²) critical value table you should use critical value the boundary of how extreme a test statistic we need to support the null hypothesis, H₀ MAT

b33d_e2df

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

chi-square (χ²) test statistic the sum of the normalized square difference between observed and expected data critical value the cutoff used to compare against the observed chi-square (χ²) test statistic level of significance, α standard cutoff probability used to determine statistic significance p-value if this value is small, then there is stronger evidence to support the alternative hypothesis, H_a degrees of freedom represents how many independent values can vary after constraints are applied MAT

b33d_232b

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

degrees of freedom represents how many independent values can vary after constraints are applied level of significance, α standard cutoff probability used to determine statistic significance chi-square (χ²) test statistic if this value is large, then there is stronger evidence to support the alternative hypothesis, H_a null hypothesis, H₀ this hypothesis makes it easier to calculate the expected values p-value the probability of getting a result that is either the same or more extreme than the actual observations MAT

b33d_8691

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

level of significance, α the fixed probability for whether or NOT the null hypothesis, H₀ can be supported null hypothesis, H₀ this hypothesis makes it easier to calculate the expected values chi-square (χ²) test statistic a measure of the discrepancy between the observed and expected data sets alternative hypothesis, H_a for this hypothesis, the expected values are often impossible to calculate p-value a small value suggests the observed data are NOT consistent with the null hypothesis, H₀ MAT

b33d_094a

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

alternative hypothesis, H_a we aim to support this hypothesis in our chi-square (χ²) test degrees of freedom this number determines which row of the chi-square (χ²) critical value table you should use null hypothesis, H₀ we attempt to find evidence against this hypothesis in our chi-square (χ²) test chi-square (χ²) test statistic if this value is large, then there is stronger evidence to support the alternative hypothesis, H_a level of significance, α a constant probability that provides a cutoff for falsification of the null hypothesis, H₀ MAT

b33d_4b68

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

p-value if this value is small, then there is stronger evidence to support the alternative hypothesis, H_a alternative hypothesis, H_a we aim to support this hypothesis in our chi-square (χ²) test null hypothesis, H₀ this hypothesis makes it easier to calculate the expected values level of significance, α a constant probability that provides a cutoff for falsification of the null hypothesis, H₀ critical value the boundary of how extreme a test statistic we need to support the null hypothesis, H₀ MAT

b33d_f921

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

p-value if this value is small, then there is stronger evidence to support the alternative hypothesis, H_a critical value found in a table for a given degrees of freedom and level of significance, α null hypothesis, H₀ we attempt to find evidence against this hypothesis in our chi-square (χ²) test alternative hypothesis, H_a we are attempting to support this hypothesis indirectly by using the chi-square (χ²) test degrees of freedom usually one less than the number of observed categories (rows in the table) MAT

b33d_9ef1

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

critical value the boundary of how extreme a test statistic we need to support the null hypothesis, H₀ level of significance, α a constant probability that provides a cutoff for falsification of the null hypothesis, H₀ chi-square (χ²) test statistic if this value is large, then there is stronger evidence to support the alternative hypothesis, H_a p-value a small value suggests the observed data are NOT consistent with the null hypothesis, H₀ alternative hypothesis, H_a we are attempting to support this hypothesis indirectly by using the chi-square (χ²) test MAT

b33d_d38b

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

degrees of freedom usually one less than the number of observed categories (rows in the table) null hypothesis, H₀ this hypothesis makes it easier to calculate the expected values p-value the smaller this number, the bigger the chi-square (χ²) test statistic critical value the boundary of how extreme a test statistic we need to support the null hypothesis, H₀ level of significance, α a fixed probability cutoff whether the null hypothesis, H₀ can be or cannot be supported MAT

b33d_89eb

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

alternative hypothesis, H_a we aim to support this hypothesis in our chi-square (χ²) test p-value if this value is small, then there is stronger evidence to support the alternative hypothesis, H_a degrees of freedom represents how many independent values can vary after constraints are applied chi-square (χ²) test statistic if this value is large, then there is stronger evidence to support the alternative hypothesis, H_a critical value found in a table for a given degrees of freedom and level of significance, α MAT

b33d_0a8e

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

chi-square (χ²) test statistic a measure of the discrepancy between the observed and expected data sets p-value the probability of getting a result that is either the same or more extreme than the actual observations degrees of freedom in most chi-square (χ²) tests, it is the number of observed categories (rows in the table) minus one level of significance, α standard cutoff probability used to determine statistic significance critical value the cutoff used to compare against the observed chi-square (χ²) test statistic MAT

b33d_8db2

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

null hypothesis, H₀ this hypothesis makes it easier to calculate the expected values degrees of freedom usually one less than the number of observed categories (rows in the table) chi-square (χ²) test statistic the bigger this number, the smaller the p-value p-value the probability of getting a result that is either the same or more extreme than the actual observations level of significance, α the fixed probability for whether or NOT the null hypothesis, H₀ can be supported MAT

b33d_74da

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

degrees of freedom this number determines which row of the chi-square (χ²) critical value table you should use critical value the cutoff used to compare against the observed chi-square (χ²) test statistic alternative hypothesis, H_a this hypothesis represents a deviation or bias from the expected pattern null hypothesis, H₀ this hypothesis makes it easier to calculate the expected values chi-square (χ²) test statistic a measure of the discrepancy between the observed and expected data sets MAT

b33d_ca15

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

alternative hypothesis, H_a if the null hypothesis, H₀ cannot be supported , then this opposing hypothesis is supported p-value the probability of getting a result that is either the same or more extreme than the actual observations chi-square (χ²) test statistic a measure of the discrepancy between the observed and expected data sets level of significance, α the statistical cutoff of the result for the null hypothesis, H₀ to be supported or not null hypothesis, H₀ this hypothesis makes it easier to calculate the expected values MAT

b33d_275d

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

alternative hypothesis, H_a for this hypothesis, the expected values are often impossible to calculate degrees of freedom in most chi-square (χ²) tests, it is the number of observed categories (rows in the table) minus one chi-square (χ²) test statistic the bigger this number, the smaller the p-value level of significance, α biologists use a probability of 0.05 (5%) for this value null hypothesis, H₀ this hypothesis makes it easier to calculate the expected values MAT

b33d_dfd1

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

p-value the smaller this number, the bigger the chi-square (χ²) test statistic chi-square (χ²) test statistic if this value is large, then there is stronger evidence to support the alternative hypothesis, H_a null hypothesis, H₀ this hypothesis makes it easier to calculate the expected values critical value found in a table for a given degrees of freedom and level of significance, α degrees of freedom usually one less than the number of observed categories (rows in the table) MAT

b33d_fa15

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

critical value the boundary of how extreme a test statistic we need to support the null hypothesis, H₀ p-value the probability of getting a result that is either the same or more extreme than the actual observations chi-square (χ²) test statistic if this value is large, then there is stronger evidence to support the alternative hypothesis, H_a degrees of freedom usually one less than the number of observed categories (rows in the table) alternative hypothesis, H_a for this hypothesis, the expected values are often impossible to calculate MAT

b33d_91a7

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

chi-square (χ²) test statistic a measure of the discrepancy between the observed and expected data sets critical value the boundary of how extreme a test statistic we need to support the null hypothesis, H₀ degrees of freedom usually one less than the number of observed categories (rows in the table) null hypothesis, H₀ this hypothesis makes it easier to calculate the expected values level of significance, α the statistical cutoff of the result for the null hypothesis, H₀ to be supported or not MAT

b33d_fbae

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

alternative hypothesis, H_a we aim to support this hypothesis in our chi-square (χ²) test degrees of freedom this number determines which row of the chi-square (χ²) critical value table you should use chi-square (χ²) test statistic the bigger this number, the smaller the p-value null hypothesis, H₀ we attempt to find evidence against this hypothesis in our chi-square (χ²) test critical value the cutoff used to compare against the observed chi-square (χ²) test statistic MAT

b33d_28cc

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

chi-square (χ²) test statistic a measure of the discrepancy between the observed and expected data sets level of significance, α the fixed probability for whether or NOT the null hypothesis, H₀ can be supported null hypothesis, H₀ we attempt to find evidence against this hypothesis in our chi-square (χ²) test critical value the cutoff used to compare against the observed chi-square (χ²) test statistic degrees of freedom in most chi-square (χ²) tests, it is the number of observed categories (rows in the table) minus one MAT

b33d_685c

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

null hypothesis, H₀ this hypothesis makes it easier to calculate the expected values alternative hypothesis, H_a we aim to support this hypothesis in our chi-square (χ²) test critical value the boundary of how extreme a test statistic we need to support the null hypothesis, H₀ level of significance, α standard cutoff probability used to determine statistic significance p-value the smaller this number, the bigger the chi-square (χ²) test statistic MAT

b33d_5baf

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

degrees of freedom this number determines which row of the chi-square (χ²) critical value table you should use p-value the smaller this number, the bigger the chi-square (χ²) test statistic null hypothesis, H₀ we attempt to find evidence against this hypothesis in our chi-square (χ²) test critical value found in a table for a given degrees of freedom and level of significance, α level of significance, α standard cutoff probability used to determine statistic significance MAT

b33d_bf4b

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

null hypothesis, H₀ we want to fail to support this hypothesis in our chi-square (χ²) test chi-square (χ²) test statistic a measure of the discrepancy between the observed and expected data sets critical value the boundary of how extreme a test statistic we need to support the null hypothesis, H₀ alternative hypothesis, H_a if the null hypothesis, H₀ cannot be supported , then this opposing hypothesis is supported degrees of freedom in most chi-square (χ²) tests, it is the number of observed categories (rows in the table) minus one MAT

b33d_d4d7

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

degrees of freedom in most chi-square (χ²) tests, it is the number of observed categories (rows in the table) minus one level of significance, α the fixed probability for whether or NOT the null hypothesis, H₀ can be supported critical value the boundary of how extreme a test statistic we need to support the null hypothesis, H₀ null hypothesis, H₀ this hypothesis makes it easier to calculate the expected values p-value the smaller this number, the bigger the chi-square (χ²) test statistic MAT

b33d_cae9

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

null hypothesis, H₀ we attempt to find evidence against this hypothesis in our chi-square (χ²) test critical value the boundary of how extreme a test statistic we need to support the null hypothesis, H₀ p-value the probability of getting a result that is either the same or more extreme than the actual observations chi-square (χ²) test statistic the bigger this number, the smaller the p-value level of significance, α biologists use a probability of 0.05 (5%) for this value MAT

b33d_7a6e

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

chi-square (χ²) test statistic if this value is large, then there is stronger evidence to support the alternative hypothesis, H_a level of significance, α a constant probability that provides a cutoff for falsification of the null hypothesis, H₀ degrees of freedom this number determines which row of the chi-square (χ²) critical value table you should use p-value the smaller this number, the bigger the chi-square (χ²) test statistic critical value found in a table for a given degrees of freedom and level of significance, α MAT

b33d_0df6

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

alternative hypothesis, H_a this hypothesis represents a deviation or bias from the expected pattern degrees of freedom in most chi-square (χ²) tests, it is the number of observed categories (rows in the table) minus one critical value found in a table for a given degrees of freedom and level of significance, α null hypothesis, H₀ we want to fail to support this hypothesis in our chi-square (χ²) test p-value if this value is small, then there is stronger evidence to support the alternative hypothesis, H_a MAT

b33d_c635

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

null hypothesis, H₀ we attempt to find evidence against this hypothesis in our chi-square (χ²) test p-value the probability of getting a result that is either the same or more extreme than the actual observations degrees of freedom represents how many independent values can vary after constraints are applied alternative hypothesis, H_a we are attempting to support this hypothesis indirectly by using the chi-square (χ²) test level of significance, α a constant probability that provides a cutoff for falsification of the null hypothesis, H₀ MAT

b33d_470b

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

null hypothesis, H₀ we want to fail to support this hypothesis in our chi-square (χ²) test degrees of freedom in most chi-square (χ²) tests, it is the number of observed categories (rows in the table) minus one alternative hypothesis, H_a this hypothesis represents a deviation or bias from the expected pattern chi-square (χ²) test statistic the bigger this number, the smaller the p-value level of significance, α a fixed probability cutoff whether the null hypothesis, H₀ can be or cannot be supported MAT

b33d_593e

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

critical value the cutoff used to compare against the observed chi-square (χ²) test statistic p-value the smaller this number, the bigger the chi-square (χ²) test statistic null hypothesis, H₀ we want to fail to support this hypothesis in our chi-square (χ²) test degrees of freedom in most chi-square (χ²) tests, it is the number of observed categories (rows in the table) minus one chi-square (χ²) test statistic a measure of the discrepancy between the observed and expected data sets MAT

b33d_4bca

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

chi-square (χ²) test statistic the bigger this number, the smaller the p-value critical value found in a table for a given degrees of freedom and level of significance, α degrees of freedom usually one less than the number of observed categories (rows in the table) level of significance, α biologists use a probability of 0.05 (5%) for this value p-value if this value is small, then there is stronger evidence to support the alternative hypothesis, H_a MAT

b33d_8173

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

alternative hypothesis, H_a we are attempting to support this hypothesis indirectly by using the chi-square (χ²) test critical value the cutoff used to compare against the observed chi-square (χ²) test statistic null hypothesis, H₀ we attempt to find evidence against this hypothesis in our chi-square (χ²) test degrees of freedom this number determines which row of the chi-square (χ²) critical value table you should use chi-square (χ²) test statistic the bigger this number, the smaller the p-value MAT

b33d_4df5

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

degrees of freedom represents how many independent values can vary after constraints are applied critical value found in a table for a given degrees of freedom and level of significance, α chi-square (χ²) test statistic if this value is large, then there is stronger evidence to support the alternative hypothesis, H_a null hypothesis, H₀ we want to fail to support this hypothesis in our chi-square (χ²) test level of significance, α a fixed probability cutoff whether the null hypothesis, H₀ can be or cannot be supported MAT

b33d_a019

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

critical value the boundary of how extreme a test statistic we need to support the null hypothesis, H₀ degrees of freedom represents how many independent values can vary after constraints are applied chi-square (χ²) test statistic the sum of the normalized square difference between observed and expected data alternative hypothesis, H_a we aim to support this hypothesis in our chi-square (χ²) test p-value the smaller this number, the bigger the chi-square (χ²) test statistic MAT

b33d_10a6

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

p-value a small value suggests the observed data are NOT consistent with the null hypothesis, H₀ null hypothesis, H₀ this hypothesis makes it easier to calculate the expected values level of significance, α the statistical cutoff of the result for the null hypothesis, H₀ to be supported or not degrees of freedom usually one less than the number of observed categories (rows in the table) chi-square (χ²) test statistic a measure of the discrepancy between the observed and expected data sets MAT

b33d_a7de

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

level of significance, α the statistical cutoff of the result for the null hypothesis, H₀ to be supported or not critical value the cutoff used to compare against the observed chi-square (χ²) test statistic chi-square (χ²) test statistic a measure of the discrepancy between the observed and expected data sets null hypothesis, H₀ this hypothesis makes it easier to calculate the expected values alternative hypothesis, H_a we are attempting to support this hypothesis indirectly by using the chi-square (χ²) test MAT

b33d_2990

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

null hypothesis, H₀ we want to fail to support this hypothesis in our chi-square (χ²) test chi-square (χ²) test statistic the sum of the normalized square difference between observed and expected data alternative hypothesis, H_a we aim to support this hypothesis in our chi-square (χ²) test p-value the probability of getting a result that is either the same or more extreme than the actual observations degrees of freedom usually one less than the number of observed categories (rows in the table) MAT

b33d_e456

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

p-value the smaller this number, the bigger the chi-square (χ²) test statistic critical value the cutoff used to compare against the observed chi-square (χ²) test statistic chi-square (χ²) test statistic if this value is large, then there is stronger evidence to support the alternative hypothesis, H_a degrees of freedom usually one less than the number of observed categories (rows in the table) level of significance, α a constant probability that provides a cutoff for falsification of the null hypothesis, H₀ MAT

b33d_cdf8

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

critical value the cutoff used to compare against the observed chi-square (χ²) test statistic level of significance, α a constant probability that provides a cutoff for falsification of the null hypothesis, H₀ p-value the smaller this number, the bigger the chi-square (χ²) test statistic degrees of freedom represents how many independent values can vary after constraints are applied null hypothesis, H₀ we attempt to find evidence against this hypothesis in our chi-square (χ²) test MAT

b33d_5dce

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

alternative hypothesis, H_a for this hypothesis, the expected values are often impossible to calculate level of significance, α the fixed probability for whether or NOT the null hypothesis, H₀ can be supported p-value the probability of getting a result that is either the same or more extreme than the actual observations degrees of freedom in most chi-square (χ²) tests, it is the number of observed categories (rows in the table) minus one chi-square (χ²) test statistic a measure of the discrepancy between the observed and expected data sets MAT

b33d_8ac0

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

level of significance, α the fixed probability for whether or NOT the null hypothesis, H₀ can be supported chi-square (χ²) test statistic the sum of the normalized square difference between observed and expected data null hypothesis, H₀ we attempt to find evidence against this hypothesis in our chi-square (χ²) test p-value a small value suggests the observed data are NOT consistent with the null hypothesis, H₀ alternative hypothesis, H_a this hypothesis represents a deviation or bias from the expected pattern MAT

b33d_aec8

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

degrees of freedom this number determines which row of the chi-square (χ²) critical value table you should use alternative hypothesis, H_a if the null hypothesis, H₀ cannot be supported , then this opposing hypothesis is supported p-value if this value is small, then there is stronger evidence to support the alternative hypothesis, H_a level of significance, α a constant probability that provides a cutoff for falsification of the null hypothesis, H₀ chi-square (χ²) test statistic the bigger this number, the smaller the p-value MAT

b33d_d9ef

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

null hypothesis, H₀ we want to fail to support this hypothesis in our chi-square (χ²) test chi-square (χ²) test statistic the sum of the normalized square difference between observed and expected data p-value the probability of getting a result that is either the same or more extreme than the actual observations degrees of freedom this number determines which row of the chi-square (χ²) critical value table you should use level of significance, α standard cutoff probability used to determine statistic significance MAT

b33d_7197

Match each of the following chi-square (χ²) terms with their corresponding definitions.

Note: Each choice will be used exactly once.

chi-square (χ²) test statistic the bigger this number, the smaller the p-value degrees of freedom in most chi-square (χ²) tests, it is the number of observed categories (rows in the table) minus one alternative hypothesis, H_a if the null hypothesis, H₀ cannot be supported , then this opposing hypothesis is supported critical value found in a table for a given degrees of freedom and level of significance, α level of significance, α a fixed probability cutoff whether the null hypothesis, H₀ can be or cannot be supported