# Meeting statistical assumptions is IMPORTANT

## Statistics is a flawed mathematical science and assumptions MUST be met

**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

__associated with using their statistical tests. There are three fundamental statistical assumptions that all researchers should check before running any type of statistic:__

**statistical assumptions**1.

__- If you are using ANY continuous variables, then use__

**Normality**__statistics to assess their normality. Any variables that have a skewness or kurtosis statistics__

**skewness and kurtosis****above an absolute value of 2.0**are assumed to be non-normal.

2.

__- If you are using between-subjects analyses to compare independent groups on a continuous outcome, then use__

**Homogeneity of variance**__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__

**Levene's test****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.