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"Statistically significant" means that the result is beyond the element of chance.

Q: Result being Statistically Significant or not statistically significant?

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A result is statistically significant if:it is unlikely to have occurred by chance

Correlation analysis. But you will need a lot more knowledge of statistics before you can decide whether the result is [statistically] significant or not, and if it is, what that means.

The F distribution is a function defined over non-negative real numbers, and it takes all sorts of values over that domain. In isolation, none of the values mean anything. An F-test, is a test based on the ratio of two variances from [approximately] normal distributions and a full interpretation requires information about the degrees of freedom. The degrees of freedom determine how much greater than 1 the value of the F-statistics can be before the result is statistically significant. However, a value near 1, such as this will not be statistically significant.

The result is 457,50 - with two significant figures.

If the conversion factor is exact, then the number of significant figures in the answer is the same as the number of significant figures in the original number.If the conversion factor is an approximation, then the number of significant figures in the result is the lesser of this number and the number of significant figures in the original number.

Related questions

A result is statistically significant if:it is unlikely to have occurred by chance

You buy a thousand lottery tickets (different numbers) and win nothing. That is statistically significant because the chances of that happening purely by chance are pretty slim. But if the lottery is operated properly, the result is not practically significant. There is nothing that can be done. Tough!

You make assumptions about the nature of the distribution for a set of observations and determine a pair of competing hypotheses - a null hypotheis and an alternative. Based on the null hypothesis you devise a test for a statistic that is based on the observations. Assuming the null hypothesis is true, if the probability of observing a test statistic that is at least as extreme as the one obtained is smaller than some pre-determined level (that is, if the observations are very unlikely under the null hypothesis) then the result is said to be statistically significant. This does not automatically imply managerial significance since, among other factors, the latter must take account of the consequences (costs) of making the wrong decision.

Correlation analysis. But you will need a lot more knowledge of statistics before you can decide whether the result is [statistically] significant or not, and if it is, what that means.

For a given experiment, and a given sample size, there is a probability that a treatment effect of a given size will yield a statistically significant finding. That is, if the treatment effect is 1 unit, then that probability (the power) might be 50%, and the power for a treatment effect of 2 units might be 75%, etc. Unfortunately, before the experiment, we don't know the treatment effect size, and indeed after the experiment we can only estimate it. So a statistically significant result means that, whatever the treatment effect size happens to be, Mother Nature gave you a "thumbs up" sign. That is more likely to happen with a large effect than with a small one.

A figure is considered significant if it represents a statistically meaningful result, typically determined by comparing it to a threshold value (e.g., p < 0.05). Significance indicates that the observed difference or relationship between variables is unlikely to have occurred by chance. Conducting statistical tests such as t-tests or ANOVAs can help determine the significance of a figure.

create hypthesis, determine testing method to evaluate and threshold needed to surpass to consider result valid in the sense of the hypothesis, test statistically significant, compare result to threshold defined, if under: drop hypothesis, if equal or above: accept as correct

The F distribution is a function defined over non-negative real numbers, and it takes all sorts of values over that domain. In isolation, none of the values mean anything. An F-test, is a test based on the ratio of two variances from [approximately] normal distributions and a full interpretation requires information about the degrees of freedom. The degrees of freedom determine how much greater than 1 the value of the F-statistics can be before the result is statistically significant. However, a value near 1, such as this will not be statistically significant.

A statement of no difference in experimental treatments indicates that there is no statistically significant effect observed between the groups being compared in an experiment. This means that the treatments did not result in a measurable difference in the outcome being studied. It suggests that any observed variations between groups could have occurred by chance and are not due to the treatments themselves.

As a result of the rule that you use the definition of the term - such as significant digits - when finding them for a number.

32.2

The appropriate number of significant figures to use in expressing the result of 51.6 x 3.1416 is three. This is because the factors each have three significant figures, so the result should also have three significant figures. The answer would be 162.