Statistically significant means that one can be confident that random chance in the sampling of the data can be rejected as the cause of the apparent difference.
Statistically significant means a p-value less than [**].
Statistically significant means six (6) or more students.
Examples of Statistically significant in a sentence
Measurement ▪ Criteria ▪ Level ▪ Period Percentage of claims dollars processed accurately 99.3% Statistically significant random sample of claims processed is reviewed to determine the percentage of claim dollars processed correctly out of the total claim dollars submitted for payment.
Measurement ▪ Criteria ▪ Level ▪ Period Percentage of claims processed without procedural (i.e. non-financial) errors 97% Statistically significant random sample of claims processed is reviewed to determine the percentage of claim dollars processed without procedural (i.e. non-financial) errors.
More Definitions of Statistically significant
Statistically significant means that a result is not likely to be due to chance alone. For purposes of this Attachment, a significance level of 0.05 or 0.01 should be used in determining statistical significance.
Statistically significant means that you can be very confident that any change between scores is real and not due to chance.
Statistically significant means the application of a Mann-Kendall analysis performed at 95 percent confidence to determine whether consecutive groundwater sampling data showing greater or lesser concentrations of constituents is statistically significant.
Statistically significant means significant as determined by ANOVA analysis of variance as applied within 40 CFR 258.53(h)(2) or as provided by 40 CFR 258.53(g)(5).
Statistically significant means that an observed difference in the sample is a true reflection of the corresponding population with 95 %
Statistically significant means that the absolute value of the bias of at least one of the tools lies outside the 90-percent confidence interval of the absolute value of the bias of the other tool.A tool can never catch up with out-of-sample/out-of-time bias. All that can be done is to construct a tool with the most-recent data, use indicators and approaches that are resistent to overfitting, and hope that the remaining (unknown) bias is not so large as to have a material effect on decisions that are informed by the estimates.For the PAT, out-of-sample/out-of-time bias has not been measured. For the scorecard, it has been measured for 11 countries. This type of bias is not discussed further here because it cannot be compared between the scorecard and the PAT.3.2.5 Out-of-group bias Dropping the second assumption and keeping the first and third leads to out-of- group bias. This matters because the scorecard and PAT are constructed from nationally representative data but are applied to non-nationally representative sub- groups. In a sub-group, the relationships between indicators and poverty generally differ from those in the construction data.31Common sub-groups are urban/rural, agricultural/non-agricultural, and sub- national regions. Of course, the participants of a pro-poor organization are a non- nationally representative sub-group, as they are both self-selected (as when a potential borrower chooses to apply for a microloan) and program-selected (as when a microlender approves loans for some potential borrowers but rejects others).
Statistically significant means that the results of an experiment most likely