Sentence examples for true hypothesis of from inspiring English sources

According to Boutron and colleagues (2007): "Blinding refers to keeping key persons, such as participants, health care providers, and outcome assessors, unaware of the treatment administered or of the true hypothesis of the trial" (0371).

The argument has a precise point in the case of tests or experiments with known error probability (the probability of rejecting a true hypothesis or of accepting a false hypothesis) but it applies quite generally: Tests of hypotheses about drug toxicity may and should have less chance of going wrong than those about the quality of a "lot of machine-stamped belt buckles".

Given that two out of six tests support the true hypothesis, the frequency of false positives among the positive findings is given by: 2/3 α / (1/3 (1−β)+2/3 α)≈0.26, and stays constant over the rounds.

For Bayesians, the Likelihood Ratio Convergence Theorem further implies the likely convergence to agreement near 0 of the posterior probabilities of false competitors of a true hypothesis.

It will be shown that provided the value of the prior probability of a true hypothesis isn't assessed to be zero, as evidence accumulates the influence of the values of the prior probabilities will very probably fade away as evidence accumulates.

In multiple testing, FWER refers to the probability of rejection of any true hypothesis.

As that happens, the community comes to agree on the refutation of these competitors, and the true hypothesis rises to the top of the heap.[25] What if the true hypothesis has evidentially equivalent rivals?

Then we have So, Thus, for any small ε > 0, (End of Proof) This theorem shows that when VQI is bounded above and EQI has a positive lower bound, a sufficiently long stream of evidence will very likely result in the refutation of false competitors of a true hypothesis.

So, not only does such evidence firm up each agent's vague initial plausibilities, it also brings the whole community into agreement on the near refutation of empirically distinct competitors of a true hypothesis.

So, support functions in collections representing vague prior plausibilities for an individual agent (i.e., a vagueness set) and representing the diverse range of priors for a community of agents (i.e., a diversity set) will very likely come to agree on the near 0 posterior probability of empirically distinct false rivals of a true hypothesis.

To increase sensitivity, we use the probabilities associated with the true hypothesis after the last round of testing rather than the fraction of correct answers.