can someone explain the null hypothesis?? plzzzz?

Question

as it relates to chi square.

Answer 1

The null hypothesis for the chi square test is that for the table with i rows and j columns you have:



H0: for each column j, p1j = p2j = p3j = ... = pij



this means that the proportion of responses in each column is the same in each row of the table. In other words, each row of the table is a independent and identically distributed multinomial random variable.



if the test returns a p-value to allow you to reject the null hypothesis it means that there is statistical evidence to support the claim that at least one of the i rows does not have the same distribuiton as the other rows.

Answer 2

I had the same exact Q, and I got this:

"The null hypothesis proposes something initially presumed true. It is rejected only when it becomes evidently false. That is, when the researcher has a certain degree of confidence, usually 95% to 99%, that the data do not support the null hypothesis."

The chi-square gives you your data

Answer 3

I reproduce decrease than the appropriate be conscious from Wikipedia which brings out the meanngs of the words and their implementation superbly. I do wish it enables you to delight in the suitable magnitude of the assumption. the alternative hypothesis (or maintained hypothesis or analyze hypothesis) and the null hypothesis are the two rival hypotheses whose likelihoods are in comparison via a statistical hypothesis attempt. many times the alternative hypothesis is the prospect that an observed consequence is genuine and the null hypothesis is the rival risk that it has resulted from random probability. The classical (or frequentist) suggestions-set is to calculate the prospect that the observed consequence (or one extra intense) will ensue if the null hypothesis is actual. If this fee (many times stated because of the fact the "p-fee") is small then the consequence is stated as statistically important and the null hypothesis is rejected in favour of the alternative hypothesis. If not, then the null hypothesis isn't rejected. Incorrectly rejecting the null hypothesis is a sort I blunders; incorrectly failing to reject this is a sort II blunders. Bayesian statisticians could project this suggestions-set in that it takes no account of a priori ideals in the two hypotheses or different outcomes of taking a incorrect determination; there may well be solid motives (extraneous to the statistical archives) for believing the null hypothesis to be remarkable. This could desire to be weighed against the damning data of a low p-fee till now the null hypothesis could nicely be rejected. An occasion: interior the trial of Sally Clark, a solicitor accused of killing the two her babies, pediatrician Sir Roy Meadow testified that the prospect of two babies interior the comparable family members dying of organic reasons grew to become right into a million in seventy 3,000,000. If organic dying is the null hypothesis and homicide the alternative hypothesis, then the p-fee is a million/seventy 3,000,000. The smallness of this fee means that the null hypothesis that the deaths had had organic reasons could be rejected and subsequently homicide concluded. the subject grew to become into that despite the fact that if the seventy 3,000,000 parent have been remarkable (this calculation grew to become into itself challenged as being incorrect via the ecological fallacy), double homicide is a uncommon adventure and there is subsequently a solid a priori reason for believing the null hypothesis. the customary hypothesis attempt grew to become into subsequently not a solid indicator of Clark's guilt

Answer 4

For any statistical test, the null hypothesis basically states that there is NO difference between your experimental group (or groups) and the control group.



If after you collect your data and analyze it (by Chi square or t-test or whatever), you find that there IS a significant difference between your experimental and control groups, then you can reject the null hypothesis (i.e. the hypothesis that there is NO difference between groups is false. Therefore, the alternative hypothesis that there IS a difference between groups is true.)



Hope this helps.