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.
In statistics, a null hypothesis is the hypothesis which you wish to test against some alternative. Often, it is framed in a way that is the opposite of what you wish to prove. You then collect the data and, if the resulting test statistic is such that observations which are at least as extreme as the one realised is very unlikely under the null hypothesis, then it is rejected and the alternative accepted.
The null hypothesis cannot be accepted. Statistical tests only check whether differences in means are probably due to chance differences in sampling (the reason variance is so important). So if the p-value obtained by the data is larger than the significance level against which you are testing, we only fail to reject the null. If the p-value is lower than the significance level, the null hypothesis is rejected in favor of the alternative hypothesis.
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.
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.
The null hypothesis will not reject - it is a hypothesis and is not capable of rejecting anything. The critical region consists of the values of the test statistic where YOU will reject the null hypothesis in favour of the expressed alternative hypothesis.
A hypothesis will be rejected if it fails the necessary testing required for it to become a scientific theory.
It is when you know that your hypothesis is wrong.
The answer to the question why is this: It can be rejected at a later date because it is falsifiable in nature if it is a good hypothesis. If you meant to ask HOW it can be rejected, the answer is by way of further experimentation that rules out some or all of the hypothesis as stated.
The hypothesis test.
no. you need to have solid proof that it exist.. else it will be rejected.
To determine whether Fleming's hypothesis should be supported or rejected based on an experiment, one would need to analyze the results of the experiment in relation to the hypothesis. If the data from the experiment aligns with the predictions made by Fleming's hypothesis, then it should be supported. However, if the results contradict the hypothesis, it may need to be rejected or revised.
H1 hypothesis is rejected when the p-value associated with the test statistic is less than the significance level (usually 0.05) chosen for the hypothesis test. This indicates that the data provides enough evidence to reject the alternative hypothesis in favor of the null hypothesis.
yes
It tells us that H1,H0 (alternative )hypothesis is selected
no
The significance test is the process used, by researchers, to determine whether the null hypothesis is rejected, in favor of the alternative research hypothesis, or not.
the reason why a rejected hypothesis can still be of value to a scientist is because that secific hyothesis may not work for your experiment but it could work for a different experiment/theory