I've read in the literature that somewhere between 30-90% of all statistics reported in the medical literature are incorrectly conducted. First of all, that's a WIDE range and either extreme should be pretty frightening to consumers of healthcare and other related services. If your practitioner is using evidence-based practices, then one would hope that your treatment regimen does NOT fall within that range!
Many times, statistics are incorrect because researchers do not check for the statistical assumptions associated with using their statistical tests. There are three fundamental statistical assumptions that all researchers should check before running any type of statistic:
1. Normality - If you are using ANY continuous variables, then use skewness and kurtosis statistics to assess their normality. Any variables that have a skewness or kurtosis statistics above an absolute value of 2.0 are assumed to be non-normal.
2. Homogeneity of variance - If you are using between-subjects analyses to compare independent groups on a continuous outcome, then use Levene's test to check for meeting the assumption of homogeneity of variance between your independent groups. This assumption assesses if the independent groups have similar variances associated with the outcome. If the p-value for Levene's test is LESS THAN .05, then the assumption has been violated.
3. "Missingness" - Missing data is a constant battle when conducting research. There are a litany of different reasons that lead to missing data but regardless, missing data can skew the results of a study by under-representation of the population of interest. If ANY of your variables have MORE THAN 20% of their observations missing, then that variable should be discarded.
Many times, statistics are incorrect because researchers do not check for the statistical assumptions associated with using their statistical tests. There are three fundamental statistical assumptions that all researchers should check before running any type of statistic:
1. Normality - If you are using ANY continuous variables, then use skewness and kurtosis statistics to assess their normality. Any variables that have a skewness or kurtosis statistics above an absolute value of 2.0 are assumed to be non-normal.
2. Homogeneity of variance - If you are using between-subjects analyses to compare independent groups on a continuous outcome, then use Levene's test to check for meeting the assumption of homogeneity of variance between your independent groups. This assumption assesses if the independent groups have similar variances associated with the outcome. If the p-value for Levene's test is LESS THAN .05, then the assumption has been violated.
3. "Missingness" - Missing data is a constant battle when conducting research. There are a litany of different reasons that lead to missing data but regardless, missing data can skew the results of a study by under-representation of the population of interest. If ANY of your variables have MORE THAN 20% of their observations missing, then that variable should be discarded.
