0Introduction

Every study, decision, or hypothesis begins with a tension between what could happen and what actually happens. The distinction between sample space and possible outcomes provides a disciplined framework for understanding causality, limitation, and interpretation.

1Defining the Problem

A problem must be conceptually clear before it can be measured. Without clarity, outcomes are observed but not understood.

2Aims and Contribution to Knowledge

Sound inquiry reduces ambiguity. A well-defined sample space ensures outcomes are interpreted within legitimate bounds.

3Existing Knowledge

Understanding prior research defines assumptions, prevents false novelty, and situates outcomes within an established context.

4Study Design

Design choices expand or constrain meaning. There are no neutral designs.

5Advantages and Limitations

All designs involve trade-offs. Recognising limitations prevents overstatement.

6Population and Sampling

Sampling defines applicability. Who is included and excluded shapes interpretation.

7Variables and Confounding

Separating variables of interest from confounders is essential to avoid false causality.

8Data Collection and Validity

Reliability and validity matter more than volume. Measurement defines meaning.

9Analysis and Interpretation

Analysis choices influence outcomes. Who analyses the data matters.

10Ethics and Consent

Ethical design defines legitimacy, not limitation.

11Resources and Feasibility

Constraints clarify what outcomes are realistically attainable.

12Communication and Application

Outcomes must be communicated responsibly to retain meaning.

13Observed Associations

Before drawing conclusions, test alternative explanations.

Apply guidelines and make a judgement proportionate to the design.