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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.
This is used in statistic to know whether to accept or reject a null hypothesis or alternative hypothesis
The null hypothesis is that there is no change in the population mean while the alternative hypothesis is that there is a change in the mean. The null hypothesis is stated as Ho:Mu=? in statistics while the alternative hypothesis is stated as Ho:Mu(<,>,≠)? depending on whether you are looking for mu to be greater, less than, or not equal to population mean.
There can be no proper answer since it is not known whether the alternative hypothesis is one sides or two sided.
When we state that the data analysis suggests that we "Reject the null hypothesis" we are stating that the sample statistic is sufficiently different from our assumed value of the population that it is unlikely to be explained by chance. If we use for example, that under the null hypothesis that engineers make on the average $120,000 per year. If we consider that the test statistic (size n) is normally distributed, we can use a two-tail test with an level of significance "alpha" to identify the lower and upper rejection zones on the normal distributon. If the test statistic falls in the non-rejection zone, we state that the "null hypothesis is not rejected." There are many good websites on hypothesis testing. Wikipedia provides a good summary of controversy on hypothesis testing. I note that some of the controversy stems from the idea that hypothesis testing will prove or validate population parameters, which is really beyond the scope of hypothesis testing theory. http://en.wikipedia.org/wiki/Statistical_hypothesis_testing A second way to determine whether the null hypotheis is to calculate p-values. For this, please see: http://en.wikipedia.org/wiki/P-value
Drawing a conclusion apex
Stating a conclusion.
Drawing a conclusion apex
Yes. But usually a hypothesis (if, then, because statement) is changed overtime to establish a conclusion on the investigation. The point of the collection of the data is to show whether or not the hypothesis was supported, and if not needs to be corrected/modified. Certain parts may still be helpful/kept but in most cases it is changed
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.
if the hypothesis is proven to be correct or incorrect
drawing a conclusion.
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.
A conclusion sums up all your observations,inference, and hypothesis in the experiment based on the data collected. This is to prove whether your hypothesis is accepted or rejected.
You cant go through the full scientific method process.(Because the 7th step is drawing conclusions & checking whether your hypothesis is right or wrong.)
No. An hypothesis is an idea put forward to explain an observation. Often you do the experiment to test the hypothesis. The results of the experiment may help you decide whether to discard your hypothesis or to test it further.
A hypothesis is a proposed explanation for a phenomenon. It is made before scientists conduct experiments or gather data to test whether it is accurate or not. The purpose of testing a hypothesis is to determine if it is supported by evidence and can be considered a valid explanation for the observed phenomenon.