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Data is statistically significant if the p (probability) value is below a certain level (ex: 5% or 1%). The p value describes how often one would receive the results they got if left to chance alone. The lower the p value, the less likely it is that your results were due to chance and is stronger evidence against the null hypothesis. Also important to keep in mind is that just because something is statistically significant does not mean it is practically significant.

Q: What does it mean for the finding of a statistical analysis of data to be statistically significant?

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The measurement of any statistical variable will vary from one observation to another. Some of this variation is systematic - due to variations in some other variable that "explains" these variations. There may be several such explanatory variables - acting in isolation or in conjunction with one another. Finally, there will be a residual variation which cannot be explained by any of these "explanatory" variables. The statistical technique called analysis of variance first calculates the total variation in the observations. The next step is to calculate what proportion of that variation can be "explained" by other variables, and finding the residual variation. A comparison of the explained variation with the residual variation is an indicator of whether or not the amount explained is statistically significant. The word "explain" is in quotes because there is not always a causal relationship. The causality may go in the opposite direction. Or the variables may be related to another variable that is not part of the analysis.

it has helped in finding the perimeters and areas of circle.

You run a post-hoc test after conducting an analysis of variance (ANOVA) and finding a significant result. A post-hoc test is used to determine which specific groups differ significantly from each other, as ANOVA only tells you that there is a difference somewhere but not which groups are different.

By using front end estimation, the numbers that you start of with have only one significant digit. It is, therefore, nonsense to have an answer with loads of significant digits.

For finding accurate volume of a pencil use a vernier calliper, which measures diameter up to two significant figures. For finding volume of a pencil you will need radius (r) and length (L) of the pencil. V = 3.14 r²L

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Statistical significance means that you are sure that the statistic is reliable. It is very possible that whatever you conclusion or finding is, it may not be important or it not have any decision-making utility. For example, my diet program has a 1 oz weight loss per month and I can show that is statistically significant. Do you really want a diet like that? It is not practically significant

The measurement of any statistical variable will vary from one observation to another. Some of this variation is systematic - due to variations in some other variable that "explains" these variations. There may be several such explanatory variables - acting in isolation or in conjunction with one another. Finally, there will be a residual variation which cannot be explained by any of these "explanatory" variables. The statistical technique called analysis of variance first calculates the total variation in the observations. The next step is to calculate what proportion of that variation can be "explained" by other variables, and finding the residual variation. A comparison of the explained variation with the residual variation is an indicator of whether or not the amount explained is statistically significant. The word "explain" is in quotes because there is not always a causal relationship. The causality may go in the opposite direction. Or the variables may be related to another variable that is not part of the analysis.

A. Hood Roberts has written the book "Black Cabin: Finding Place in Appalachia."

Why is normal distribution important in statistical analysis? Why is normal distribution important in statistical analysis? An important statistical effect was named for this manufacturing plant. What is it? In a famous research study conducted in the years 1927-1932 at an electrical equipment manufacturing plant, experimenters measured the influence of a number of variables (brightness of lights, temperature, group pressure, working hours, and managerial leadership) on the productivity of the employees. The major finding of the study was that no matter what experimental treatment was employed, the production of the workers seemed to improve. It seemed as though just knowing that they were being studied had a strong positive influence on the workers. .The Hawthorne effect

Statistically analysis. Finding areas under the curves of normal distributions. Look in the back of any statistics text at the t scores. All done for you by calculus. Statistics are vital in many walks of life, from business to science.

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.

Actually, this would require a graphic illustrations and various complex mathematical formulas to adequately explain the whole process. Any statistical analysis text could do this job quite well.

Leonard H. Zacks is known for his work in quantitative finance and statistical analysis. He has written several books on investment strategies, particularly focusing on using statistical methods to improve stock market prediction and analysis. Some of his popular titles include "The Handbook of Equity Market Anomalies" and "Finding #1 Stocks."

Analysis means finding the exact scenario for the problem and design means finding the main class from the analysis part an d to give operation for that class. and from that we can know the exact process.

It is a statistical function specifically, finding a number that is a good representation of a set of numbers.

Who? What? Where? When? How? and also Why? are the questions.

Is the analysis of a problem and finding the root cause or reason for the problem