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.
There is an established statistical point for most comparisons or measurements that is so small that differences at or below it are considered to be "random", "predictable", or "meaningless". If a difference between A and B exceeds this point, it is said to be "significant", which does not necessarily mean "important" or "huge" - just "significant".
It is the likelihood of any particular event occurring.
The term statistically valid means a study is able to draw conclusions that are in agreement with statistical and scientific laws. This relies on mathematical and statistical laws.
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.
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.
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.
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.
Statistically significant is the term used to define when two data are distinct enough in value as to be considered different values. To determine whether two data are close enough in value or distinct enough in value to be considered the same or different, usually you have to do a p-test or a t-test, depending on the type of data that you are looking at. Then confer with the corresponding chart for the test that you did to see whether or not the data is statistically significant.
There is an established statistical point for most comparisons or measurements that is so small that differences at or below it are considered to be "random", "predictable", or "meaningless". If a difference between A and B exceeds this point, it is said to be "significant", which does not necessarily mean "important" or "huge" - just "significant".
It is the likelihood of any particular event occurring.
The term statistically valid means a study is able to draw conclusions that are in agreement with statistical and scientific laws. This relies on mathematical and statistical laws.
Case closed statistically means something different for each situation. If a parent or teacher says case closed it means that there will be no argument. In cases of the law, it statistically means that the case has ended and a verdict has been reached.
Statistically speaking, the mean is the most stable from sample to sample. Whereas, the mode is the least stable statistically speaking from sample to sample.
It is not clear what you mean by the word "power" in this context, but a significant use for a spreadsheet is to automatically calculate complex equations and perform mathematical functions on long lists of numbers.
u do know that that question makes no sense? i think u left out a little bit at the end.............. u mean an ed?
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.