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Uncertainty may be implied by the last significant figure if it is not explicitly expressed. [1] The implied uncertainty is ± the half of the minimum scale at the last significant figure position. For example, if the mass of an object is reported as 3.78 kg without mentioning uncertainty, then ± 0.005 kg measurement uncertainty may be implied.
A volumetric pipette, bulb pipette, or belly pipette [1] allows extremely accurate measurement (to four significant figures) of the volume of a solution. [2] It is calibrated to deliver accurately a fixed volume of liquid.
That g-PDF is plotted with the histogram (black line) and the agreement with the data is very good. Also shown in Figure 2 is a g-PDF curve (red dashed line) for the biased values of T that were used in the previous discussion of bias. Thus the mean of the biased-T g-PDF is at 9.800 − 0.266 m/s 2 (see Table 1).
Therefore, there are 3 significant figures can be read from the given graduated cylinder picture. [9] Another example, if the reading is done and the value calculated is set to be 40.0 mL. The precise value is 40.0 0.1; 40.1 or 39.9 mL. [10]
Print/export Download as PDF; Printable version; In other projects ... Standard errors provide simple measures of uncertainty in a value and are often used because:
(B) A tall-form or Berzelius beaker (C) A flat beaker or crystallizer Philips beaker which can be swirled like a conical flask. Standard or "low-form" (A) beakers typically have a height about 1.4 times the diameter. [3] The common low form with a spout was devised by John Joseph Griffin and is therefore sometimes called a Griffin beaker.
Significant figures, the digits of a number that carry meaning contributing to its measurement resolution Topics referred to by the same term This disambiguation page lists articles associated with the title SigFig .
Probability bounds analysis gives the same answer as interval analysis does when only range information is available. It also gives the same answers as Monte Carlo simulation does when information is abundant enough to precisely specify input distributions and their dependencies. Thus, it is a generalization of both interval analysis and ...