It is measurement on an ordinal scale. Level 1 is less than level 2 which is less than level 3 and so on. But the difference between levels 1 and 2 is not related to the difference between levels 2 and 3, etc.
A physician wishes to study the relationship between hypertension and smoking habits. From a random sample of 180 individuals, the following results were obtainedAt the 5% level of significance, test whether the absence of hypertension is independent of smoking habits.HypertensionSmoking habitNon-smokersModerate smokersHeavy smokersYes213630No482619
What is the importance of the level of significance of study findings in a quantitative research report
The significance level is always small because significance levels tell you if you can reject the null-hypothesis or if you cannot reject the null-hypothesis in a hypothesis test. The thought behind this is that if your p-value, or the probability of getting a value at least as extreme as the one observed, is smaller than the significance level, then the null hypothesis can be rejected. If the significance level was larger, then statisticians would reject the accuracy of hypotheses without proper reason.
"Better" is subjective. A 0.005 level of significance refers to a statistical test in which there is only a 0.5 percent chance that a result as extreme as that observed (or more extreme) occurs by pure chance. A 0.001 level of significance is even stricter. So with the 0.001 level of significance, there is a much better chance that when you decide to reject the null hypothesis, it did deserve to be rejected. And consequently the probability that you reject the null hypothesis when it was true (Type I error) is smaller. However, all this comes at a cost. As the level of significance increases, the probability of the Type II error also increases. So, with the 0.001 level of significance, there is a greater probability that you fail to reject the null hypothesis because the evidence against it is not strong enough. So "better" then becomes a consideration of the relative costs and benefits of the consequences of the correct decisions and the two types of errors.
P- value is the probability that the given null hypothesis is true and the level of significance is the chance in a hundred or thousand off occurence of an event i an outcome
what is the difference between elementary and basic
difference between business level strategy and corporate level strategy?
what is the difference between Re oreder level and EOQ
It's like the difference between a biopsy and an autopsy.
it is difference between the water level from head race and tail race
A hypothesis statement consists of three parts: the null hypothesis (H0), the alternative hypothesis (Ha), and the level of significance (alpha). The null hypothesis states that there is no relationship or difference between variables, while the alternative hypothesis suggests the presence of a relationship or difference. The level of significance determines the threshold for accepting or rejecting the null hypothesis based on statistical testing.
run each regressors as dependent variable and leave constant as independent variable. in the next window, click down Tests and choose Durbin Watson. This should give you the DW coefficient and its corresponding pvalue. reject null if less than significance level, accept if more than.
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the level of wealth
A significance level of 0.05 is commonly used in hypothesis testing as it provides a balance between Type I and Type II errors. Setting the significance level at 0.05 means that there is a 5% chance of rejecting the null hypothesis when it is actually true. This level is widely accepted in many fields as a standard threshold for determining statistical significance.
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