Independent & Identically Distributed (IDD)

Option	Description
Required	Strict independence and identical distribution are assumed for valid estimation and inference.
Recommended	Verifying approximate IID behavior is advised to ensure the robustness of results.
Desirable	While not strictly mandatory, satisfying IID greatly simplifies modeling and improves interpretability.
Relaxes assumption	Methods that can directly accommodate dependence (e.g., mixed models, GEE) reduce reliance on IID.

Definition

Whether the method assumes observations are independent and identically distributed (IID): Each data point in the sample is drawn independently from the same underlying probability distribution, with no systematic correlation or clustering among observations.

Explanation

IID underlies the validity of many classical statistical estimators, assuming observations neither influence one another nor come from different regimes. When this assumption is violated (e.g., temporal autocorrelation, spatial clustering, hierarchical structure), specialized methods like mixed‐effects models, clustered standard errors, or time‐series techniques must be employed to obtain unbiased inference.

Tools/rationale for helping assessment

1. Reflect on your sampling design: were plots assigned/visited independently and identically?
2. If sampling is truly random and independent, mark Required; if small clustering exists, Recommended; if clustering is common but manageable, Desirable; if strong dependencies exist and/or want to study this assumption’s influence on results, Relaxes assumption.

Example

You randomly placed 50 camera traps across a landscape - Required IID holds. If some traps are on the same trail, minor clustering → Recommended. If they follow animal trails with strong spatial dependence, you likely shall relax the IID assumption.