Sample size determination involves identifying the quantity of data required to conduct accurate statistical analysis. When working with large datasets it is not always necessary to use the entire set. We use sample size determination to ensure we choose a sample small enough to manipulate and analyze easily, but large enough to represent our population of data accurately.
It is not uncommon to use a subset of data to train a model and another subset is used to test the model. This can be helpful for verifying accuracy and reliability of data. Some common consequences for a poorly determined sample size include false-positive results, false-negative results, identifying statistical significance where none exists, or suggesting a lack of significance where it is actually present. Many tools exist online for determining appropriate sample sizes, each with varying levels of complexity. One simple example is available at https://www.surveymonkey.com/mp/sample-size-calculator...