true or false. In a hypothesis test, the alternate hypothesis is assumed to be true until data shows it to be improbable.
False. In hypothesis testing, it is the null hypothesis, not the alternate hypothesis, that is assumed to be true to start with. The alternate hypothesis represents a statement that is tested against the null hypothesis.
The null hypothesis (denoted as H0) is a statement of no effect or no difference, and it is presumed true until evidence suggests otherwise. The alternate hypothesis (denoted as Ha or H1) represents the effect or difference that the test is designed to detect.
Data collected from experiments or observations are used to assess the likelihood that the null hypothesis is true. If the data show that the null hypothesis is highly improbable (usually by calculating a p-value and comparing it to a pre-established significance level, alpha), then the null hypothesis is rejected in favor of the alternate hypothesis. However, failing to reject the null hypothesis does not necessarily prove it true; it simply means there isn't sufficient evidence against it given the data and the test performed.