Understanding Statistical Errors
The dataset used in a study is tabulated and analyzed using statistical methods and techniques to achieve the desired goal and objectives. A hypothesis statement summarizes the goals by stating whether or not there is a substantial difference between the groups or treatments being investigated.
Null (Ho) and alternative (H1) hypotheses are formulated to test the presence of a statistical difference between two groups or treatments. The sample statistics used to estimate the population parameters are randomly chosen hence statistical errors may arise from chance, biased data, and variability. The two types of statistical errors are type I and type II.
Type 1 error
Type I error (α) also referred to as false positive is rejecting the null hypothesis when it is true. False-positive concludes the presence of difference hence the treatment under study is effective. A confidence interval of 95% provides a 5% threshold for statistical significance, a probability of type I error occurrence. Type I error occurs purely from a function of chance.
Causes of Type I Error
Conducting multiple testing on a single dataset increases the chances of type I error occurrence indicating a false difference.
P-value ˃0.05 used increases the margin of error and significance threshold under which the study lies. A small sample size results in a large effect size increasing the difference between the groups or treatments.
Type II Error
Type II error (β) is accepting a null hypothesis when there are no differences between the groups or treatments.
β is arbitrary set at 20% meaning detecting a difference in a sample that exists in the larger population has an 80% chance of being precise. Type II error is a function of power (1-β). The main cause of type II error occurrence is the small sample size.
Minimizing Statistical Errors
Statistical errors can be eluded by avoiding multiple testing in treatments and groups. Different comparisons should be made separately to reduce the chances of falsely detecting a significant difference.
An adequate sample size should be used because a large sample size results in a smaller effect size increasing the accuracy and validity of the dataset used in the study. Using stringent p-values increases the significance of the difference. A 99% confidence interval has a 1% probability of type I error occurring compared to 95% that has a 5% probability threshold.
Summary
Statistical errors can lead to incorrect research conclusions, compromising the accuracy and validity of the data used. Rejecting a null hypothesis when it is true suggests the presence of a difference between the treatment while accepting a null hypothesis when there is no difference proposes the treatments are not effective.
Factors such as small sample size, multiple testing, and p-value used are the causes of statistical errors. Multiple testing increases the chances of statistical errors. Large sample sizes reduce the chance of a statistical error occurring by minimizing the effect size.
The confidence interval used determines the margin of error, the larger the confidence interval, the smaller the threshold or probability of wrongly concluding research.