Question

what is chi-square and what does hypothesis testing testing tell us. please explain in great detail for a thumbs up !

Answer 1

Hypothesis testing and chi square testing are used to determine certain population parameters by analysing the parameters of sample. Hypothesis testing is based on the case of rejecting or accepting the Null hypothesis.

Chi square statistic conclude its result on the basis of closenes of observed and expected frequencies or parameters.

Hypothesis testing gives us an idea about the certain parameters of the population by analysing the same parameter of a sample drawn from the population. It is simply a conjecture about a characteristic or a set of characteristics. It involves testing the difference between a hypothesized value of population parameter and the same parameter calculated from the sample. If difference is large me reject the hypothesis and if difference is small we accept the hypothesis. The hypothesis to be tested The hypothesis to be tested is called the Null hypothesis (Ho) and the alternative hypothesis is called alternative hypothesis (they. Suppose The mean age of the citizens is to yous. The mean age of a group of citizens is togs. on sample mean is re years. In this case Ho: H-70 He: H t 70

For best results we calculate 2 stara - - & -sample mean 42 Population mean or standard deviation If our of sample z calculated x acceptance is so in regection - Zvable interval me ngect the hypothesized interval mean of 70 you, of population and accept the alternative hypothests and rejection interfon interval cautical +7c rejection vice versa. Thus we Population can draw parameter a conclusion from hypothesis about testing, chi square test (82) This test is used to estimate wh how closely à sample matches the expected distribution (also known as the goodness of fit test) white estimating the two random variables independent of one another The value that indicates the comparison between the observed and expected frequency is called the square statistic . The idea is that if observed and expected value are closer 2 will be small,

Formula x = q (0-E2 x= chi-square statistic - O= observed frequency G = Expected frequency We compare the value of the test statistic to a tabled-chi-square value to determine the probability that if a sample fits an. expected pattern ar not Just like Hypothesis testing if x² valle fits the interval we accept that expected and observed frequencies/ datas are closely related and vice Versa acceptance region vection rigusion

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