how do you determine the significant difference between two groups?
550436
Usually, statistical significance is determined by calculating the probability of error (p value) by the t ratio. The difference between two groups (such as an experiment vs. control group) is judged to be statistically significant when p = 0.05 or less.
6927
['Mean of Sample is the same as the mean of the population.', 'The standard deviation which is calculated is the same as the standard deviation of the population divided by the square root of the sample size.']
193276
['Work out the Mean (the simple average of the numbers)', 'Then for each number: subtract the Mean and square the result (the squared difference).', 'Then work out the average of those squared differences. (Why Square?)']
695729
['Collect as Many Measurements of the Thing You Are Measuring as Possible. ... ', 'Find the Average Value of Your Measurements. ... ', 'Find the Absolute Value of the Difference of Each Individual Measurement from the Average.']
1328174
['Subtract the two values (real and measured). ... ', 'Next, divide the difference by the actual or ideal value to obtain a decimal number.']
1436585
To determine whether the correlation between variables is significant, compare the p-value to your significance level. Usually, a significance level (denoted as α or alpha) of 0.05 works well. An α of 0.05 indicates that the risk of concluding that a correlation exists—when, actually, no correlation exists—is 5%.
1233636
['If you have some values in two columns, and now, you want to compare them row by row to highlight the differences as following screenshot shown. ... ', 'Select the data that you want to compare two columns and highlight the differences, and then click Home > Find & Select > Go To Special, see screenshot:']
1055245
There are many ways to do this. A simple approach could be to group the continuous variable using the categorical variable, measure the variance in each group and comparing it to the overall variance of the continuous variable.
440893
['Find the mean of the data set. Add all data values and divide by the sample size n.', 'Find the squared difference from the mean for each data value. Subtract the mean from each data value and square the result.', 'Find the sum of all the squared differences. ... ', 'Calculate the variance.']
616726
['START counting for sig. figs. On the FIRST non-zero digit.', 'STOP counting for sig. figs. On the LAST non-zero digit.', 'Non-zero digits are ALWAYS significant.', 'Zeroes in between two non-zero digits are significant. All other zeroes are insignificant.']
287347
There are three major ways of determining statistical significance: If you run an experiment and your p-value is less than your alpha (significance) level, your test is statistically significant.
1056235
The difference between two rational numbers, a/b and c/d, is equal to the result of subtracting the smaller number from the larger number. To find the difference between rational numbers, or to subtract rational numbers, we use the following formula: a/b - c/d = (ad - bc) / bd.
1450549
['diff = x2 - x1; % x2 and x1 are your input variables. x1 is reference and x2 the value to compare.', 'relDiff = diff / x1;', 'if abs(relDiff) > tolerance % e.g. 0.01 for 1%', "disp('good');", 'else.', "disp('bad');", 'end.']
1252066
['You simply subtract both columns and display it as column like below query.', 'SELECT col1,col2,(col1-col2) as col3 FROM table;', 'This will display the difference in third column.']
280940
When a difference between two groups is statistically significant (e.g., the difference in selection rates is greater than two standard deviations), it simply means that we don't think the observed difference is due to chance.
1377016
The mean difference, or difference in means, measures the absolute difference between the mean value in two different groups. In clinical trials, it gives you an idea of how much difference there is between the averages of the experimental group and control groups.
556324
Find the difference (subtract) between the accepted value and the experimental value, then divide by the accepted value. To determine if a value is precise find the average of your data, then subtract each measurement from it. This gives you a table of deviations.