Hypotheses and falsifiability

A hypothesis is a claim that can be wrong — and you say in advance what would show that.

• «Our model is good» — not falsifiable.
• «Our model reaches ≥ 85 % on test set X» — falsifiable: run it and see.
• «Dropout 0.5 reduces the train–test gap by at least 5 percentage points compared with 0, over three seeds» — falsifiable and precise.

The trap: explaining after the fact. The result came out at 83 %? «Well, the test set was hard.» If every outcome can be explained the hypothesis has no content. Write the prediction down before you run.

Rewrite these as falsifiable hypotheses:

Vague	Falsifiable
More data helps	Doubling the training data raises the validation accuracy by ≥ 2 pp (3 seeds, the mean)
The model understands sarcasm	On 200 sarcastic examples ≥ 70 % right, against 50 % for chance
The prompt is better	On the same 100 cases, prompt B wins ≥ 60 % of the pairwise comparisons

For each: what is the outcome that disproves it? If you cannot write that down the hypothesis is not testable. Then write it in the lab journal before the run — with a date.