Inaccurate tools can lead to both random error and systematic error. Random error increases the variability of the data, making it harder to detect true associations. Systematic error, or bias, can lead to consistently incorrect estimates, which can misinform public health policies and interventions. For example, an inaccurate tool might overestimate the prevalence of a disease, leading to unnecessary allocation of resources to combat a non-existent epidemic.
