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The term that refers to findings that are "probably true" or "not due to chance" is indeed significant. In statistical analysis, the notion of significance typically relates to the results of a study being statistically significant, indicating that the observed effects or relationships are unlikely to have occurred by random chance alone. This is assessed using p-values, confidence intervals, or hypothesis testing, which help determine whether to reject the null hypothesis.

When results are deemed significant, it suggests that there is a strong enough evidence base to support the claim that the observed data reflects a real effect or correlation in the population, rather than something that could happen merely by random fluctuations in samples. This concept is crucial in research, as it helps to validate findings and supports informed decision-making based on statistical evidence.

The other terms listed do not capture this specific statistical meaning. Usefulness refers to the practical applicability of results, importance pertains to the value or weight of findings in context, and randomness characterizes an outcome that occurs without a discernible pattern or structure, which is the opposite of what is implied by significant findings.