Continuum hypothesis was proven, with an proving method called "forcing", to be undecidable under commonly accepted axioms of the set theory. This means that neither continuum hypothesis nor it's negation follows from this axioms just like one axiom (or it's negation) in some consistent axiomatic system does not follow from other axioms. Therefore, continuum hypothesis or it's negation could be added as an additional axiom to existing commonly accepted axioms of the set theory.
false
If we reject the null hypothesis, we conclude that the alternative hypothesis which is the alpha risk is true. The null hypothesis is used in statistics.
W The test statistic is is the critical region or it exceeds the critical level. What this means is that there is a very low probability (less than the critical level) that the test statistics could have attained a value as extreme (or more extreme) if the null hypothesis were true. In simpler terms, if the null hypothesis were true you are very, very unlikely to get such an extreme value for the test statistic. And although it is possible that this happened purely by chance, it is more likely that the null hypothesis was wrong and so you reject it.
Expected frequencies are used in a chi-squared "goodness-of-fit" test. there is a hypothesis that is being tested and, under that hypothesis, the random variable would have a certain distribution. The expected frequency for a "cell" is the number of observations that you would expect to find in that cell if the hypothesis were true.
We have two types of hypothesis i.e., Null Hypothesis and Alternative Hypothesis. we take null hypothesis as the same statement given in the problem. Alternative hypothesis is the statement that is complementary to null hypothesis. When our calculated value is less than the tabulated value, we accept null hypothesis otherwise we reject null hypothesis.
A hypothesis is a statement.
False- The hypothesis is your prediction of what you expect to happen. If the data does not agree with your hypothesis you simply explain why your hypothesis did not come true and possibly investigate variable which would allow your hypothesis to come true.
False- The hypothesis is your prediction of what you expect to happen. If the data does not agree with your hypothesis you simply explain why your hypothesis did not come true and possibly investigate variable which would allow your hypothesis to come true.
No. A hypothesis is an educated guess, based on observation. Usually, a hypothesis can be supported or refuted through experimentation or more observation. A hypothesis can be disproven, but not proven to be true.
An aleph-one is the second of the transfinite cardinal numbers, which, according to the continuum hypothesis, corresponds to the number of real numbers.
True because the point of the hypothesis test is to figure out the probability of the null hypothesis being true or false. If it is tested and it is true, then you do not reject but you reject it, when it is false.
in some cases it's true. if it is true in your case, this means your hypothesis was a successful one.
It is the hypothesis that is presumed true until statistical evidence in the form of a hypothesis test proves it is not true.
It is the hypothesis that is presumed true until statistical evidence in the form of a hypothesis test proves it is not true.
Rejecting a true null hypothesis.
It is true.
a. the hypothesis ispartly true but needs to be revised. b. the hypothesis wrong. c. the hypothesis is supported. d. the hypothesis is of no value.