yes
It must be either, otherwise it is systematic error or bias.
if you meant by absolute error, then yes.
No because taking the absolute value of a number always yields a positive value.
No, the absolute error cannot be negative. Absolute error is defined as the absolute value of the difference between the measured value and the true value, which ensures that it is always non-negative. It is calculated as |measured value - true value|, and since absolute values are always positive or zero, the absolute error itself will also never be negative.
Divide the calculated or estimated error by the magnitude of the measurement. Take the absolute value of the result, that is, if it is negative, convert to positive. This would make the percent error = | error / measurement |.
The level of significance; that is the probability that a statistical test will give a false positive error.
Positive zero error means, instead showing zero it shows some value more than zero. Hence positive. Suppose if it shows some reading say 0.03 units. then while correcting we have to subtract the above from the observed reading. So correction is adding negative error.
Its distance from zero, always a positive number. The absolute value of a positive number is that number. The absolute value of a negative number is its positive equivalent. Usually denoted by vertical bars |n| The absolute value of both 7 and -7 is 7 |-7| = 7 |7| = 7 * * * * * Minor error above: the absolute value of 0 is 0, so not "always a positive number".
Depending on whether you subtract actual value from expected value or other way around, a positive or negative percent error, will tell you on which side of the expected value that your actual value is. For example, suppose your expected value is 24, and your actual value is 24.3 then if you do the following calculation to figure percent error:[percent error] = (actual value - expected value)/(actual value) - 1 --> then convert to percent.So you have (24.3 - 24)/24 -1 = .0125 --> 1.25%, which tells me the actual is higher than the expected. If instead, you subtracted the actual from the expected, then you would get a negative 1.25%, but your actual is still greater than the expected. My preference is to subtract the expected from the actual. That way a positive error tells you the actual is greater than expected, and a negative percent error tells you that the actual is less than the expected.
Given a true value and the measured value,the error is measured value - true value;the relative error is (measured value - true value)/true value, andthe percentage error is 100*relative error.
Percent Error is the difference between the true value and the estimate divided by the true value and the result is multiplied by 100 to make it a percentage. The percent error obviously can be positive or negative; however, some prefer taking the absolute value of the difference. The formula is the absolute value of the experimental value (minus) the theoretical value divided by theoretical value times 100. % error = (|Your Result - Accepted Value| / Accepted Value) x 100
Systematic error is a constant or known:effects of the error are cumulativeerror is always positive or negativeAccidental error is a unavoidable error: effects of the error is compensationerror is equally like to be positive or negative