how do you understand null hypothesis and alternative hypothesis?
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The hypothesis that the estimate is based solely on chance is called the null hypothesis. Thus, the null hypothesis is true if the observed data (in the sample) do not differ from what would be expected on the basis of chance alone. The complement of the null hypothesis is called the alternative hypothesis.
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Both the null and alternative hypothesis should be stated before any statistical test of significance is conducted. In other words, you technically are not supposed to do the data analysis first and then decide on the hypotheses afterwards.
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The null hypothesis states that there is no difference in mean scores between the two groups. The nondirectional alternative hypothesis states that there is a difference between the mean scores of two groups but does not specify which group is expected to be larger or smaller.
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In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).
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The null hypothesis states that a population parameter (such as the mean, the standard deviation, and so on) is equal to a hypothesized value. ... The alternative hypothesis states that a population parameter is smaller, greater, or different than the hypothesized value in the null hypothesis.
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In contrast, a directional alternative hypothesis specifies the direction of the tested relationship, stating that one variable is predicted to be larger or smaller than null value, but not both. ... The null hypothesis states that there is no difference in mean scores between the two groups.
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A t-test is a statistical test that is used to compare the means of two groups. ... You can test the difference between these two groups using a t-test. The null hypothesis (H0) is that the true difference between these group means is zero. The alternate hypothesis (Ha) is that the true difference is different from zero.
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If the P-value is less than (or equal to) , then the null hypothesis is rejected in favor of the alternative hypothesis. And, if the P-value is greater than , then the null hypothesis is not rejected.
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Statistical analysts test a hypothesis by measuring and examining a random sample of the population being analyzed. All analysts use a random population sample to test two different hypotheses: the null hypothesis and the alternative hypothesis. ... However, one of the two hypotheses will always be true.
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The null hypothesis is generally denoted as H0. It states the exact opposite of what an investigator or an experimenter predicts or expects. It basically defines the statement which states that there is no exact or actual relationship between the variables. The alternative hypothesis is generally denoted as H1.
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Accepting the null hypothesis would indicate that you've proven an effect doesn't exist. As you've seen, that's not the case at all. You can't prove a negative! Instead, the strength of your evidence falls short of being able to reject the null. Consequently, we fail to reject it.
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To determine whether any of the differences between the means are statistically significant, compare the p-value to your significance level to assess the null hypothesis. The null hypothesis states that the population means are all equal. Usually, a significance level (denoted as α or alpha) of 0.05 works well.
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The null is not rejected unless the hypothesis test shows otherwise. The null statement must always contain some form of equality (=, ≤ or ≥) Always write the alternative hypothesis, typically denoted with Ha or H1, using less than, greater than, or not equals symbols, i.e., (≠, >, or <).
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The null and alternative hypotheses are two mutually exclusive statements about a population. A hypothesis test uses sample data to determine whether to reject the null hypothesis. ... The alternative hypothesis is what you might believe to be true or hope to prove true.
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Statistical analysts test a hypothesis by measuring and examining a random sample of the population being analyzed. All analysts use a random population sample to test two different hypotheses: the null hypothesis and the alternative hypothesis.
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There are two hypotheses that are made: the null hypothesis, denoted H0, and the alternative hypothesis, denoted H1or HA. The null hypothesis is the one to be tested and the alternative is everything else.
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Research hypothesis may be Null or Alternative. For example if a researcher want to test new drug against old one and thinks that newer is superior then alternate is research hypothesis. But if researcher thinks that the newer drug is not so effective then Null hypothesis is research hypothesis.