Null and Alternative Hypotheses for a Mean

In the χ2 goodness-of-fit test, we conclude that either the distribution specified in H0 is false (when we reject H0) or that we do not have sufficient evidence to show that the distribution specified in H0 is false (when we fail to reject H0). Here, we reject H0 and concluded that the distribution of responses to the exercise question following the implementation of the health promotion campaign was not the same as the distribution prior. The test itself does not provide details of how the distribution has shifted. A comparison of the observed and expected frequencies will provide some insight into the shift (when the null hypothesis is rejected). Does it appear that the health promotion campaign was effective?

We then have three null hypotheses (and three alternatives).

There are two hypotheses in statistical analysis: the null (H0)and the research hypothesis (H1).

Null hypothesis: μ = 72 Alternative hypothesis: μ ≠72

The p-value is p = 0.019. This is below the .05 standard, so the result is statistically significant. This means we decide in favor of the alternative hypothesis. We're deciding that the population mean is not 72.

Null hypothesis: μ = 72 Alternative hypothesis: μ ≠72

We must first assess whether the sample size is adequate. Specifically, we need to check min(np0, np1, ..., n pk) > 5. The sample size here is n=470 and the proportions specified in the null hypothesis are 0.60, 0.25 and 0.15. Thus, min( 470(0.65), 470(0.25), 470(0.15))=min(282, 117.5, 70.5)=70.5. The sample size is more than adequate so the formula can be used.

“The smaller the P-value computed from the sample data, the stronger the evidence is against the null hypothesis.” See also

T-test | Stata Annotated Output

Stata calculates the difference () as , or proportion of non-religious people who answered true minus proportion of religious people who answered true. Thus the hypothesis that religious people are less likely to answer true is and the very low p-value associated with it suggests we should reject the null and accept that alternative hypothesis.

Stata | FAQ: One-sided tests for coefficients

Alternatively, a two-tailed prediction means that we do not make a choice over the direction that the effect of the experiment takes. Rather, it simply implies that the effect could be negative or positive. If Sarah had made a two-tailed prediction, the alternative hypothesis might have been:

We fail to reject the null hypothesis at the 0.05 significance level since the value of the Levene test statistic is less than the critical value.

Single sample t-Test in Stata without hardcoded null hypothesis

Rank the absolute value of the differences between observations from smallest to largest, with the smallest difference getting a rank of 1, then next larger difference getting a rank of 2, etc. Give average ranks to ties. Add the ranks of all differences in one direction, then add the ranks of all differences in the other direction. The smaller of these two sums is the test statistic, W (sometimes symbolized Ts). Unlike most test statistics, smaller values of W are less likely under the null hypothesis. For the aluminum in wood example, the median change from August to November (3.1 micrograms Al/g wood) is significantly different from zero (W=16, P=0.040).

, and rejecting the null hypothesis when the P-value is less than the significance level

For all these tests we've described the null hypothesis.

Test statistic: = 20.78580Degrees of freedom: - 1 = 9Significance level: = 0.05Critical value: 21-,-1 = 16.919Critical region: Reject H0 if > 16.919We are testing the null hypothesis that the batch variances are allequal.

Hypothesis Testing - Signifinance levels and rejecting or accepting the null hypothesis

Null hypothesis for multiple linear regression - SlideShare

Example:Suppose that we want to test the hypothesis with a significance level of that the climate has changed since industrializatoin.