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What is Statistical Significance?

The for a given hypothesis test is a value for which a less than or equal to is considered statistically significant.

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Statistical significance does not mean practical significance.

Fortunately, you can easily determine the statistical significance of experiments, without any math, using , the advanced statistical model built-in to Optimizely.

Here’s a recap of statistical significance:

After you do a statistical test, you are either going to reject or accept the null hypothesis. Rejecting the null hypothesis means that you conclude that the null hypothesis is not true; in our chicken sex example, you would conclude that the true proportion of male chicks, if you gave chocolate to an infinite number of chicken mothers, would be less than 50%.

Now say statistically significant three times fast.

- A 39 0.46 6.87 1.10 0.42 0.34The P-Value of 0.34 indicates that this result is not significant at any acceptablelevel.

Since sample size is so important in making statistical inferences, your committee naturally wants to be sure that your dissertation research uses an adequate sample size to effectively address your research questions. That is why you should include a "sample size justification" section in your .

Power Analysis is a family of statistical procedures which are used to justify the appropriate sample size for testing a given statistical hypothesis.

Statistical significance - Wikipedia

Power Analysis is a family of statistical procedures which are used to justify the appropriate sample size for testing a given statistical hypothesis.

Since sample size is so important in making statistical inferences, your committee naturally wants to be sure that your dissertation research uses an adequate sample size to effectively address your research questions. That is why you should include a "sample size justification" section in your .

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  • What does "statistical significance" really mean

    Statistically significant.

  • P-value, significance level and hypothesis

    In principle, a statistically significant result (usually a difference) is a result that’s not attributed to chance.

  • 26/06/2017 · How to Assess Statistical Significance

    19/03/2015 · Understanding Hypothesis Tests: Significance Levels (Alpha) and P values in Statistics

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Sample Size | Statistically Significant Consulting

My tutorial page provides a good explanation of the basic principles of probability and statistical inference. If you understand those concepts, you will understand why sample size is so important to the effectiveness of your study.

sample size for testing a given statistical hypothesis ..

Except that... there is a justification for one-tailed tests after all. You just interpret the p value differently. P values for one-tailed tests are half those for two-tailed tests. It follows that the p value from a one-tailed test is the exact probability that the true value of the effect has opposite sign to what you have observed, and 1 - p is the probability that the true value of the effect has the same sign, as I explained . Hey, we don't have to muck around with p/2. So here's what you could write in the Methods section of your paper: "All tests of significance are one-tailed in the direction of the observed effect. The resulting p values represent the probability that the true value of the effect is of sign opposite to the observed value." Give it a go and see what happens. Such a statement would be anathema to reviewers or statisticians who assert that an observed positive result is not a justification for doing a one-tailed test for a positive result. They would argue that you are downgrading the criterion for deciding what is "statistically significant", because you are effectively performing tests with a Type I error of 10%. Fair enough, so don't mention statistical significance at all. Just show 95% confidence limits, and simply say in the Methods: "Our p values, derived from one-tailed tests, represent the probability that the true value of the effect is of sign opposite to the observed value."

Statistical Significance - Palaeo-Electronica

My tutorial page provides a good explanation of the basic principles of probability and statistical inference. If you understand those concepts, you will understand why sample size is so important to the effectiveness of your study.

T-Test Statistical Significance Example and Definition

Statistical significance is a measure of whether your research findings are meaningful. More specifically, it’s whether your stat closely matches what value you would expect to find in an entire . In order to test for statistical significance, perform these steps:

An explanation of statistical significance in the context ..

Whilst there is relatively little justification why a significance level of 0.05 is used rather than 0.01 or 0.10, for example, it is widely used in academic research. However, if you want to be particularly confident in your results, you can set a more stringent level of 0.01 (a 1% chance or less; 1 in 100 chance or less).

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