It means that the experiment is consistent with the hypothesis. It adds to the credibility of the hypothesis.
the hypothesis has not been proven wrong.
alternitive hypothesis
In order to solve this you need the null hypothesis value also level of significance only helps you decide whether or not to reject the null hypothesis, is the p-value is above this then you do not reject the null hypothesis, if it is below you reject the null hypothesis Level of significance has nothing to do with the math
Sampling distribution is crucial in hypothesis testing as it provides the distribution of a statistic, such as the sample mean, under the null hypothesis. By understanding the sampling distribution, researchers can determine the likelihood of obtaining their observed sample statistic if the null hypothesis is true. This allows for the calculation of p-values, which indicate the probability of observing the data given the null hypothesis. Ultimately, this helps in making informed decisions about whether to reject or fail to reject the null hypothesis.
The term hypothesis is used in science and statistics. I have included two links related to the these terms.In statistics, the null and alternative hypothesis are mathematical statements used in statistical decision making. An example of a null hypothesis is the mean of the population from which a sample was obtained is equal to 10. The mean of the data is sufficiently different from 10 can be used to reject the null hypothesis.As used in science, hypothesis is the initial idea suggested by observation or preliminary experimentation. See related links.
It means there is no reason why he should reject it, whether because there is no evidence to the contrary or because an experiment set up to test it affirmed that hypothesis.
the hypothesis has not been proven wrong.
It means that she or he has to accept that the existing hypothesis appears to be true.
It means there is no reason why he should reject it, whether because there is no evidence to the contrary or because an experiment set up to test it affirmed that hypothesis.
It means there is no reason why he should reject it, whether because there is no evidence to the contrary or because an experiment set up to test it affirmed that hypothesis.
alternitive hypothesis
That he either found it to be incorrect or heard from a majority of other scientists working in the same field of study that the hypothesis isn't true. Though it's almost always the first one I said.
When a scientist rejects a hypothesis, it means that the data or evidence does not support the initial proposed explanation for a phenomenon. This rejection prompts the scientist to reconsider the hypothesis, gather more data, or formulate a new hypothesis that better fits the observed results.
In order to solve this you need the null hypothesis value also level of significance only helps you decide whether or not to reject the null hypothesis, is the p-value is above this then you do not reject the null hypothesis, if it is below you reject the null hypothesis Level of significance has nothing to do with the math
Sampling distribution is crucial in hypothesis testing as it provides the distribution of a statistic, such as the sample mean, under the null hypothesis. By understanding the sampling distribution, researchers can determine the likelihood of obtaining their observed sample statistic if the null hypothesis is true. This allows for the calculation of p-values, which indicate the probability of observing the data given the null hypothesis. Ultimately, this helps in making informed decisions about whether to reject or fail to reject the null hypothesis.
The term hypothesis is used in science and statistics. I have included two links related to the these terms.In statistics, the null and alternative hypothesis are mathematical statements used in statistical decision making. An example of a null hypothesis is the mean of the population from which a sample was obtained is equal to 10. The mean of the data is sufficiently different from 10 can be used to reject the null hypothesis.As used in science, hypothesis is the initial idea suggested by observation or preliminary experimentation. See related links.
The null hypothesis in a chi-square goodness-of-fit test states that the sample of observed frequencies supports the claim about the expected frequencies. So the bigger the the calculated chi-square value is, the more likely the sample does not conform the expected frequencies, and therefore you would reject the null hypothesis. So the short answer is, REJECT!