Based upon our sample evidence, there’s a 1% chance we will claim our new teaching strategy is more effective when it is not more effective in the population beta () probability of a Type 2 error; researchers often strive to set this rate .20 or less Example There’s a 20% chance we will claim there is no relationship between hours studied and achievement when there is a relationship in the population There’s a 10% chance we will claim our new teaching strategy is not more effective when it is more effective in the population power () probability of identifying an effect if one exists; probability of rejecting a false null hypothesis; complement of beta () so it is the probability of not committing a Type 2 error Example There’s an 80% chance we will detect a relationship between hours studied and achievement in our sample if there is a relationship in the population With this small sample there is only a 40% chance we will correctly claim our new teaching strategy is more effective when it is more effective n is study sample size Example In our pregnancy we tested 15 doctors to determine if they could correctly identify pregnancy status of patients To test our new teaching strategy, we sampled 108,637 students; 46,714 students received the new teaching strategy and 61,923 received current standard instructional approaches effect size effect size denotes the magnitude of difference if comparing means or magnitude of relationship between variables may be unstandardized or standardized four commonly used ESs d: mean difference of two groups ES d is defined as the mean difference between two groups divided by a standard deviation which is commonly the pooled SD, but could be other SDs, so
Based upon our sample evidence, there’s a 1% chance we will claim our new teaching strategy is more effective when it is not more effective in the population beta () probability of a Type 2 error; researchers often strive to set this rate .20 or less Example There’s a 20% chance we will claim there is no relationship between hours studied and achievement when there is a relationship in the population There’s a 10% chance we will claim our new teaching strategy is not more effective when it is more effective in the population power () probability of identifying an effect if one exists; probability of rejecting a false null hypothesis; complement of beta () so it is the probability of not committing a Type 2 error Example There’s an 80% chance we will detect a relationship between hours studied and achievement in our sample if there is a relationship in the population With this small sample there is only a 40% chance we will correctly claim our new teaching strategy is more effective when it is more effective n is study sample size Example In our pregnancy we tested 15 doctors to determine if they could correctly identify pregnancy status of patients To test our new teaching strategy, we sampled 108,637 students; 46,714 students received the new teaching strategy and 61,923 received current standard instructional approaches effect size effect size denotes the magnitude of difference if comparing means or magnitude of relationship between variables may be unstandardized or standardized four commonly used ESs d: mean difference of two groups ES d is defined as the mean difference between two groups divided by a standard deviation which is commonly the pooled SD, but could be other SDs, so