Why the Scientific Method Generally Does Not Work:

Confirmation bias:
The tendency to unconsciously seek, favour, or interpret data that supports your hypothesis while ignoring or dismissing contradictory evidence. Research shows this often operates implicitly, shaping choices about comparators and outcomes without the researcher realising it.

P-hacking (data dredging or data snooping):
The practice of running many statistical tests or analyses on the same data and reporting only the ones that come back "significant." Even with no real effect, enough repeated tests will eventually produce a false positive by chance. A related practice, HARKing (Hypothesizing After Results are Known), involves formulating the hypothesis after seeing the data, then presenting it as if it were pre-registered.

Publication bias (or the "file drawer problem"):
Studies with positive or significant results are far more likely to be published than null or negative ones, skewing the literature and making effects look larger than they really are.

Sampling and measurement bias:
Flawed or unrepresentative samples, uncalibrated instruments, inconsistent data collection procedures, and failure to account for confounding variables can all invalidate results even when the overall process looks correct.

Neglecting controls and replication:
A single experiment without proper control groups, or one that cannot be independently reproduced, provides weak evidence. The replication crisis in psychology and other fields has highlighted how many published findings fail to reproduce.

Conceptual errors:
Confusing a hypothesis with a theory (a theory is a well-supported, broad explanation; a hypothesis is a specific, testable prediction), or treating findings as fixed truths rather than the best current explanation subject to revision.

Mistaking correlation for causation:
Observing that two variables move together and concluding one causes the other, without ruling out alternative explanations.

In short, the method is only as good as the discipline applied at each stage, and most of these pitfalls stem from human cognitive tendencies, structural incentives (e.g., pressure to publish positive results), or poor experimental design, including deliberate fraud.

📱 ROBINMG