Statistical conclusion validity concerns the qualities of the study that make these types of errors more likely.
[2][3][4] The most common threats to statistical conclusion validity are: Power is the probability of correctly rejecting the null hypothesis when it is false (inverse of the type II error rate).
If a researcher searches or "dredges" through their data, testing many different hypotheses to find a significant effect, they are inflating their type I error rate.
The more the researcher repeatedly tests the data, the higher the chance of observing a type I error and making an incorrect inference about the existence of a relationship.
These threats to internal validity include unreliability of treatment implementation (lack of standardization) or failing to control for extraneous variables.