What is the difference between a false positive and a false negative?

Short Answer

Expert verified

A false positive is an error that shows up when results are interpreted as positive (e.g. something is present when it's actually not, something is detected as above the limit when it's actually below the limit), while a false negative says the other way around (e.g. Something is identified as being below the limit when it is actually above the limit, and something is recognised as being absent when it is truly present).

Step by step solution

01

Definition of false positive and false negative.

False positive:

In analytical Chemistry, False positive is an assumption that the concentration of analyte goes above a certain limit, in fact the concentration is below the limit.

False negative:

In analytical Chemistry, False negative is an assumption that the concentration of analyte is lower than a certain limit. In fact, the concentration is above the limit.

02

Difference between false positive and false negative

When you get a positive test result when you should have gotten a negative result, it's known as a false positive. It's also known as a "false positive error" or "false alarm." It's most commonly employed in the medical area, but it can be applied to other fields as well (like software testing).

Examples:

  • A pregnancy test indicates that you are pregnant when you are not.
  • You have a positive cancer screening test, but you don't have the disease.
  • A prenatal test for Down's Syndrome comes up positive even though your foetus does not have the disorder.
  • Your computer's virus software wrongly classifies a safe programme as harmful.

When a negative test result is incorrect, it is referred to as a false negative.To put it another way, you get a negative test result when you should have had a positive one. For instance, suppose you take a pregnancy test and it comes back negative (not pregnant). You are, however, expecting a child. It's possible that you got a false negative on a pregnancy test because you took it too soon, used diluted urine, or checked the findings too quickly. A false negative is a possibility with almost every diagnostic test. A cancer test, for example, could come back negative when you truly have the condition.

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Most popular questions from this chapter

Control chart. A laboratory monitoring perchlorate (CIO-4)in urine measured quality control samples made from synthetic urine spiked withCIO-4. The graph in Box5-2shows consecutive quality control measurements. Are any troubleshooting conditions from Box 5-2 observed in these data?

Correlation coefficient and Excel graphing. Synthetic

data are given below for a calibration curve in which random Gaussian

noise with a standard deviation ofwas superimposed on

y values for the equationy=26.4x+1.37

. This exercise shows that

a high value ofR2

does not guarantee that data quality is excellent.

(a)Enter concentration in column A and signal in column B of a

spreadsheet. Prepare an XY Scatter chart of signal versus concentration

without a line as described in Section2-11

. Use LINEST

(Section 4-7) to find the least-squares parameters includingR2

.

(b)Now insert the Trendline by following instructions on page 88.

In the Options window used to select the Trendline, select Display

Equation and Display R-Squared. Verify that Trendline and LINEST

give identical results.

(c)Add 95%confidence interval y error bars following the instructions

at the end of Section 4-9. The95%

confidence interval is 6tsy,

wheresy

comes from LINEST and Student’s tcomes from Table 4-4

for95%confidence and11-2=9

degrees of freedom. Also, compute

t with the statement "=TINV(0.05,9)".

Internal standard graph -- Data are shown below for chromatographic analysis of naphthalene (C10H8), using deuterated naphthalene (C10D8, in which D is the isotope 2H) as an internal standard. The two compounds emerge from the column at almost identical times and are measured by a mass spectrometer.

(a) Using a spreadsheet such asFigure 4-15, prepare a graph of Equation 5-12 showing peak area ratio(C10H8/C10D8)versus concentration ratio role="math" localid="1663559632352" ([C10H8]/C10D8) . Find the least-squares slope and intercept and their standard uncertainties. What is the theoretical value of the intercept? Is the observed value of the intercept within experimental uncertainty of the theoretical value?

(b) Find the quotientrole="math" localid="1663559638520" [C10H8]/[C10D8]for an unknown whose peak area ratio (C10H8/C10D8) is 0.652. Find the standard uncertainty for the peak area ratio.

(c) Here is why we try not to use 3-point calibration curves. For n = 3 data points, there is n - 2 = 1 degree of freedom, because 2 degrees of freedom are lost in computing the slope and intercept. Find the value of Student's for confidence and 1 degree of freedom. From the standard uncertainty in (b), compute the 95 % confidence interval for the quotient[C10H8]/[C10D8] . What is the percent relative uncertainty in the quotient[C10H8]/[C10D8]? Why do we avoid 3-point calibration curves?

1.00 mL of blood serum was diluted to 25.00 mL in each flask of a standard addition experiment like Figure 5-7 to measure a hormone with a molecular mass of 373 g/mol. The x-intercept of the graph was 4.2 ppb (parts per billion). Find the concentration of hormone in the serum and express your answer in ppb and molarity. Assume that the density of serum and all solutions is close to 1.00 g/mL

State when standard additions and internal standards, instead of a calibration curve, are desirable, and why.

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