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When the observed sample mean is close to expected population mean what do you in terms of the null hypothesis?
Wiki User
∙ 11y ago
It depends on whether the hypothesis concerns the mean or the standard error (or variance) or something else.
Wiki User
∙ 11y ago
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How much error is expected between the sample mean and population mean?
0. The expected value of the sample mean is the population mean, so the expected value of the difference is 0.
What is hypothesis of difference?
The alternativehypothesis (Ha or H1) describes the population parameters that the sample data represent, if the predicted relationship exists. It is always the hypothesis of difference. That is as opposed to the null hypothesis (H0) that describes the population parameters that the sample data represent if the predicted relationship does not exist. See Basic Statistics of the Behavioral Sciences by Heiman.
What is the difference between the goodness of fit test and the contingency test?
The goodness of fit test is used to determine if a sample comes from a specific population by comparing the observed frequencies to the expected frequencies. It is used to test if there is a significant difference between the observed data and the expected data. On the other hand, the contingency test, also known as the chi-square test of independence, is used to determine if there is an association between two categorical variables by comparing the observed frequencies in a contingency table to the frequencies that would be expected if the variables were independent. It is used to assess if there is a significant relationship between the two variables.
How do you prove that the sample variance is equal to the population variance?
You cannot prove it because it is not true.The expected value of the sample variance is the population variance but that is not the same as the two measures being the same.
What do you mean when you reject a hypothesis on the basis of sample?
alternitive hypothesis
If the chi-square is very large what does it mean?
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!
How much error is expected between the sample mean and population mean?
0. The expected value of the sample mean is the population mean, so the expected value of the difference is 0.
What does the chi-square goodness of fit test determine?
The chi-square goodness of fit test determines whether a set of categorical data have an expected value that is similar to the observed value. For example, if you hypothesized that each Zodiac animal sign has the same proportion of people, then you would look at a sample. You would organize that sample into a chart showing how many people fit into which Zodiac animal. This is your observed value. Then you would have another table to express what your expected value is, which is 1/12 of the total sample population (because there are 12 Zodiac animal signs). Your null hypothesis is that each Zodiac animal sign will have 1/12 of the population. Next, you verify that you can use the chi-square test. To use the chi-square test you must verify that all your expected counts are over 5. Afterwards, you need to calculate a special variable notated as x^2. For each animal, you take the ((observed-expected)^2) / expected. Then you add it up. This is your x^2. Afterwards, you find the P-Value by using a chart or a calculator. If your P-Value is small, this means that the sample could not have occurred by chance and as a result, you can reject your null hypothesis. You would have significant evidence to prove that each Zodiac animal contains a different proportion. If your p-value is large, you fail to reject your null hypothesis.
The closer the sample mean is to the population mean?
Your question is a bit difficult to understand. I will rephrase: In hypothesis testing, when the sample mean is close to the assumed mean of the population (null hypotheses), what does that tell you? Answer: For a given sample size n and an alpha value, the closer the calculated mean is to the assumed mean of the population, the higher chance that null hypothesis will not be rejected in favor of the alternative hypothesis.
How do you know in question of hypothesis that there is population SD or sample SD?
da fac?
What value does the null hypothesis make a claim about?
sample statistic
What is the meaning of null hypthesis being rejected?
You may want to prove that a given statistic of a population has a given value. This is the null hypothesis. For this you take a sample from the population and measure the statistic of the sample. If the result has a small probability of being (say p = .025) if the null hypothesis is correct, then the null hypothesis is rejected (for p = .025) in favor of an alternative hypothesis. This can be simply that the null hypothesis is incorrect.
What is hypothesis of difference?
The alternativehypothesis (Ha or H1) describes the population parameters that the sample data represent, if the predicted relationship exists. It is always the hypothesis of difference. That is as opposed to the null hypothesis (H0) that describes the population parameters that the sample data represent if the predicted relationship does not exist. See Basic Statistics of the Behavioral Sciences by Heiman.
As the sample size increases what does the expected value of M do?
You have not defined M, but I will consider it is a statistic of the sample. For an random sample, the expected value of a statistic, will be a closer approximation to the parameter value of the population as the sample size increases. In more mathematical language, the measures of dispersion (standard deviation or variance) from the calculated statistic are expected to decrease as the sample size increases.
What is the difference between the goodness of fit test and the contingency test?
The goodness of fit test is used to determine if a sample comes from a specific population by comparing the observed frequencies to the expected frequencies. It is used to test if there is a significant difference between the observed data and the expected data. On the other hand, the contingency test, also known as the chi-square test of independence, is used to determine if there is an association between two categorical variables by comparing the observed frequencies in a contingency table to the frequencies that would be expected if the variables were independent. It is used to assess if there is a significant relationship between the two variables.
What are quantitative techniques?
Many of the quantitative techniques fall into two broad categories: # Interval estimation # Hypothesis tests Interval Estimates It is common in statistics to estimate a parameter from a sample of data. The value of the parameter using all of the possible data, not just the sample data, is called the population parameter or true value of the parameter. An estimate of the true parameter value is made using the sample data. This is called a point estimate or a sample estimate. For example, the most commonly used measure of location is the mean. The population, or true, mean is the sum of all the members of the given population divided by the number of members in the population. As it is typically impractical to measure every member of the population, a random sample is drawn from the population. The sample mean is calculated by summing the values in the sample and dividing by the number of values in the sample. This sample mean is then used as the point estimate of the population mean. Interval estimates expand on point estimates by incorporating the uncertainty of the point estimate. In the example for the mean above, different samples from the same population will generate different values for the sample mean. An interval estimate quantifies this uncertainty in the sample estimate by computing lower and upper values of an interval which will, with a given level of confidence (i.e., probability), contain the population parameter. Hypothesis Tests Hypothesis tests also address the uncertainty of the sample estimate. However, instead of providing an interval, a hypothesis test attempts to refute a specific claim about a population parameter based on the sample data. For example, the hypothesis might be one of the following: * the population mean is equal to 10 * the population standard deviation is equal to 5 * the means from two populations are equal * the standard deviations from 5 populations are equal To reject a hypothesis is to conclude that it is false. However, to accept a hypothesis does not mean that it is true, only that we do not have evidence to believe otherwise. Thus hypothesis tests are usually stated in terms of both a condition that is doubted (null hypothesis) and a condition that is believed (alternative hypothesis). Website--http://www.itl.nist.gov/div898/handbook/eda/section3/eda35.htmP.s "Just giving info on what you don't know" - ;) Sillypinkjade----
If the difference between the observed and expected frequencies if the critical value is 9.488 and the computed value is 6.079 do you reject or do not reject the null hypothesis?
Not enough information - nature of step progression towards critical value has to be specified (sample size, linear vs. logarithmic vs. whatever, etc.).
