- Sensitivity: A/(A + C) × 100 10/15 × 100 = 67%; The test has 53% specificity. In other words, 45 persons out of 85 persons with negative results are truly negative and 40 individuals test positive for a disease which they do not have. Specificity: D/(D + B) × 100 45/85 × 100 = 53%; The sensivity and specificity are characteristics of this test
- Sensitivity and specificity are statistical measures of the performance of a binary classification test that are widely used: Sensitivity (True Positive rate) measures the proportion of positives that are correctly identified (i.e. the proportion of those who have some condition (affected) who are correctly identified as having the condition)
- In a test with high sensitivity, a positive is positive. Specificity refers to the ability of a test to rule out the presence of a disease in someone who does not have it. 1 In other words, in a test with high specificity, a negative is negative
- Relationship between
**Sensitivity****and****Specificity**In medical tests,**sensitivity**is the extent to which actual positives are not overlooked (so false negatives are few), and**specificity**is the extent to which actual negatives are classified as such (so false positives are few) - The equation to calculate the sensitivity of a diagnostic test The specificity is calculated as the number of non-diseased correctly classified divided by all non-diseased individuals. So 720 true negative results divided by 800, or all non-diseased individuals, times 100, gives us a specificity of 90%
- But there are so many performance metrics to look at, which one do you choose? How do you interpret the values? Let's look at a commonly used method for classification models called the confusion matrix. We cover accuracy, sensitivity, specificity, precision & f1 score
- Three very common measures are accuracy, sensitivity, and specificity. Accuracy is one of those rare terms in statistics that means just what we think it does, but sensitivity and specificity are a little more complicated. To understand all three, first we have to consider the situation of predicting a binary outcome

- The sensitivity of the test reflects the probability that the screening test will be positive among those who are diseased. In contrast, the specificity of the test reflects the probability that the screening test will be negative among those who, in fact, do not have the disease
- • Interpreting the result of a test for covid-19 depends on two things: the accuracy of the test, and the pre-test probability or estimated risk of disease before testing • A positive RT-PCR test for covid-19 test has more weight than a negative test because of the test's high specificity but moderate sensitivity
- e how useful antibody test results are when making health care decisions:•Clinical sensitivity deter
- Sensitivity is the percentage of true positives (e.g. 90% sensitivity = 90% of people who have the target disease will test positive). Specificity is the percentage of true negatives (e.g. 90% specificity = 90% of people who do not have the target disease will test negative)
- When we are reading about a diagnostic test we are going to find terms which define their value, such as sensitivity and specificity. In this post I am going to define them in simple words to make them clear and easy to interpret, so after you read this you can put them into practice

- Sensitivity vs Specificity mnemonic. SnNouts and SpPins is a mnemonic to help you remember the difference between sensitivity and specificity.; SnNout: A test with a high sensitivity value (Sn) that, when negative (N), helps to rule out a disease (out).; SpPin: A test with a high specificity value (Sp) that, when positive (P) helps to rule in a disease (in)
- Both sensitivity and specificity as well as positive and negative predictive values are important metrics when discussing tests. If you would like to read further into this topic, we recommend starting with Receiver Operating Characteristic (ROC) curves
- When 400 µg/L is chosen as the analyte concentration cut-off, the sensitivity is 100 % and the specificity is 54 %. When the cut-off is increased to 500 µg/L, the sensitivity decreases to 92 % and the specificity increases to 79 %. An ROC curve shows the relationship between clinical sensitivity and specificity for every possible cut-off
- The sensitivity and specificity tradeoff. For any test, there is usually a tradeoff between avoiding false positives and false negatives. For example, in airport metal detectors looking for a gun, if the machine is extremely sensitive, individuals carrying virtually any metal will set off the detector (i.e., low false negatives)
- Evaluating the results of an antigen test for SARS-CoV-2 should take into account the performance characteristics (e.g., sensitivity, specificity) and the instructions for use of the FDA-authorized assay, the prevalence of SARS-CoV-2 infection in that particular community (positivity rate over the previous 7-10 days or the rate of cases in.
- Accuracy= (Sensitivity + Specificity)/2. In the case where, the number of excellent candidates and poor performers are equal, if any one of the factors, Sensitivity or Specificity is high then Accuracy will bias towards that highest value
- Sensitivity and Specificity. PPV of mammograms for breast cancer is said to range from 4.3% to 52.4% depending on the expertise of the radiologist interpreting the image.).

- Sensitivity is the metric that evaluates a model's ability to predict true positives of each available category. Specificity is the metric that evaluates a model's ability to predict true negatives of each available category. These metrics apply to any categorical model. The equations for calculating these metrics are below
- The sensitivity, specificity and likelihood ratios are properties of the test. The positive and negative predictive values are properties of both the test and the population you test. If you use a test in two populations with different disease prevalence, the predictive values will be different
- Sensitivity and Specificity Sensitivity indicates how often a diagnostic test detects a disease or condition when it is present. Sensitivity essentially tells the clinician how good the test is at correctly identifying patients with the condition of interest
- ation) would be a useful screening test for cervical cancer
- Even w/ 90% sensitivity and specificity, if base rate is relatively low (condition is rare), the majority of individuals who exhibit that sign or test score will not have the condition. EXAMPLE: In unreferred population of 1,000 children and 4% base rate for ADHD, 40 children are expected to have ADHD
- Sensitivity: 88.2%. Specificity: 22.2%. AUC: 0.628. How can I interpret the high sensitivity and low specificity?! machine-learning classification supervised-learning. Share. Cite. Improve this question. Follow asked May 23 '19 at 15:24. learneRS learneRS. 145 1 1 silver badge 11 11 bronze badge

Sensitivity vs specificity mnemonic. SnNouts and SpPins is a mnemonic to help you remember the difference between sensitivity and specificity. SnNout: A test with a high sensitivity value (Sn) that, when negative (N), helps to rule out a disease (out) Sensitivity and specificity, positive and negative predictive values, and positive and negative likelihood ratios are common indicators of diagnostic test accuracy Because percentages are easy to understand we multiply sensitivity and specificity figures by 100. We can then discuss sensitivity and specificity as percentages. So, in our example, the sensitivity is 60% and the specificity is 82%. This test will correctly identify 60% of the people who have Disease D, but it will also fail to identify 40% How do you read sensitivity and specificity? Medical examples. In medical diagnosis, test sensitivity is the ability of a test to correctly identify those with the disease (true positive rate), whereas test specificity is the ability of the test to correctly identify those without the disease (true negative rate) Sensitivity and Specificity are displayed in the LOGISTIC REGRESSION Classification Table, although those labels are not used. In the classification table in LOGISTIC REGRESSION output, the observed values of the dependent variable (DV) are represented in the rows of the table and predicted values are represented by the columns

Q: What do sensitivity and specificity mean in antibody testing? Sensitivity is the ability of the test to identify people with antibodies to SARS-CoV-2. This is known as the true positive rate The illustrations used earlier for sensitivity and specificity emphasized a focus on the numbers in the left column for sensitivity and the right column for specificity. If this orientation is used consistently, the focus for predictive value is on what is going on within each row in the 2 x 2 table, as you will see below 7.1 Calculating Estimates of Sensitivity and Specificity To help interpret these measures, we recommend you provide the definition of condition of interest, the reference standard, the. The mnemonics SnOut and SpIn provide some guidelines on how to interpret sensitivity and specificity for an individual patient. SnOut helps physicians to remember that a highly S ensitive test with a n egative result is good at ruling out the disease * The plot shows how the sensitivity increases as the specificity decreases and vice versa, in relation to the possible cutoff points of the biomarker*. Mann-Whitney U test statistic The Mann-Whitney U test statistic (or Wilcoxon or Kruskall-Wallis test statistic) is equivalent to the AUC (Mason, 2002)

Estimation of sensitivity and specificity at fixed specificity and sensitivity: compile a table with estimation of sensitivity and specificity, with a BC a bootstrapped 95% confidence interval (Efron, 1987; Efron & Tibshirani, 1993), for a fixed and prespecified specificity and sensitivity of 80%, 90%, 95% and 97.5% (Zhou et al., 2002) Positive and negative predictive values are actually much more helpful than sensitivity and specificity for a clinician to interpret the data. Essentially, we want to know what the probability of disease is given a positive or negative test result. But we often see different specialists interpret the same lab values in a very different way code; then moving on to the common issues on interpreting the results of sensitivity , specificity and accuracy; ended by a final remark of the entire paper. 2. SENSITIVITY, SPECIFICITY AND ACCURACY, 95% CINFIDENCE INTERVAL AND ROC CURVE 2.1 SENSITIVITY, SPECIFICITY AND ACCURACY Condition (e.g. Disease) As determined by the Standard of Trut # of people who do not have # people specificity specificity disease and test negative by BOTH = who do not X of the X of the the physician and the cardiologist have disease physician cardiologis t = 1800 X.80 X.90 = 1298 Net specificity = 1298 / 1800 = 72% As compared to the net sensitivity for sequential testing, the net sensitivity for.

In this post, we will try and understand the concepts behind evaluation metrics such as sensitivity and specificity, which is used to determine the performance of the Machine Learning models.The. Sensitivity and specificity data are recapitulated elsewhere. 4 The use of an absolute delta change is superior to the percentage change because it provides a changing set of criteria depending on the baseline value, thus preserving sensitivity. When the absolute or relative delta is less than conjoint biological and analytical variation, some. The relation between Sensitivity, Specificity, FPR, and Threshold. Sensitivity and Specificity are inversely proportional to each other. So when we increase Sensitivity, Specificity decreases, and vice versa. Sensitivity⬆️, Specificity⬇️ and Sensitivity⬇️, Specificity⬆️. When we decrease the threshold, we get more positive.

Interpreting Home Pregnancy Tests. To correctly interpret home pregnancy tests, it is essential to know the sensitivity, specificity, and positive and negative predictive values for the test when performed by individuals without any medical or laboratory medicine training Specificity: the probability that the model predicts a negative outcome for an observation when indeed the outcome is negative. One easy way to visualize these two metrics is by creating a ROC curve , which is a plot that displays the sensitivity and specificity of a logistic regression model

It is here that both, the Sensitivity and Specificity, would be the highest and the classifier would correctly classify all the Positive and Negative class points. Understanding the AUC-ROC Curve in Python. Now, either we can manually test the Sensitivity and Specificity for every threshold or let sklearn do the job for us. We're definitely. Table 4 indicates the results of sensitivity, specificity, positive predictive value, and negative predictive value for CHEMM-IST. The result showed the sensitivity was from .84 to .97. The specificity was from, .29 to .45. The positive predictive value and negative predictive value were from .18 to 42, and .86 to .97; respectively senting di erent alpha levels (i.e., di erent emphases on sensitivity or speci city; Lin & Dayton 1997). This perspective may lead to insights about how to interpret the criteria in less simple situations. For ex-ample, AIC or BIC could be preferable, depending on sample size and on the relative importance one assigns to sensitivity versus. Trouble remembering how to calculate sensitivity and specificity of a screening or diagnostic test from a 2x2 table? Here's an easy way to remember. Make sur..

4 min read; Recall, Specificity, Precision, F1 Scores and Accuracy Recall or Sensitivity or True Positive Rates. When false positives are zero the Specificity will be 1, which is a highly specific model. Easy way to remember its formula is that we need to focus on Actual Negatives as in the diagram of Specificity Commenting on the ever-increasing sensitivity and decreasing specificity of cTn assays, Robert Jesse quipped, When troponin was a lousy assay it was a great test, but now that it's becoming a great assay, it's getting to be a lousy test. 9 However, frequent monitoring of cTn kinetics, along with careful attention to the noncoronary causes.

Within the context of screening tests, it is important to avoid misconceptions about sensitivity, specificity, and predictive values. In this article, therefore, foundations are first established concerning these metrics along with the first of several aspects of pliability that should be recognized in relation to those metrics. Clarification is then provided about the definitions of. Specificity: The probability that the model predicts a negative outcome for an observation when indeed the outcome is negative. One easy way to visualize these two metrics is by creating a ROC curve, which is a plot that displays the sensitivity and specificity of a logistic regression model

The positive and negative predictive values (PPV and NPV respectively) are the proportions of positive and negative results in statistics and diagnostic tests that are true positive and true negative results, respectively. The PPV and NPV describe the performance of a diagnostic test or other statistical measure. A high result can be interpreted as indicating the accuracy of such a statistic Sensitivity and specificity are characteristic of the test, while predictive values are influenced by the prevalence of the disease in the tested population. Read more Articl Sensitivity and specificity do not provide educators with any level of confidence that, for this particular student, he or she will fail or pass the PSSA. Given the limitations associated with sensitivity and specificity interpretation, educators are called to compute and interpret positive and negative likelihood ratios (LRs). LRs are th The manufacturer of QuantiFERON-TB Gold+ conducted seven sensitivity studies and four specificity studies across multiple sites in the United States, do not require training to interpret, and. Interactive simulation of sensitivity and specificity. The graph displays the distributions of healthy and diseased patients on a certain hypothetical test (e.g. fasting blood sugar values for the diagnosis of diabetes). You can adjust the separation between the two distributions as well as their spreads (i.e. how much variability there is within each distribution)

and the sensitivity is 25 0.93 25 2 = +. In‐class Problem: Calculate specificity and sensitivity when the cut point is 5. In‐class Activity Complete Table 2 using the data in Table 1 (see spreadsheet). From the table you can see that there is a tradeoff between specificity and sensitivity Sensitivity (%) Specificity (%) Bacterial Culture of fecal samples 60 ± 5 99.9 ± 0.1 PCR assay of fecal samples 30 ± 5 99.5 ± 0.5 ELISA on serum or milk 30 ± 5 99.0 ± 1.0 Evaluation of biopsy specimens 90.5 ± 5 100 Necropsy 100 100 * The test Sensitivity and Specificity are averaged numbers from the literature The ROC curve shows the trade-off between sensitivity (or TPR) and specificity (1 - FPR). Classifiers that give curves closer to the top-left corner indicate a better performance. As a baseline, a random classifier is expected to give points lying along the diagonal (FPR = TPR) Sensitivity and Specificity. By changing the threshold, the good and bad customers classification will be changed hence the sensitivity and specificity will be changed; Which one of these two we should maximize? What should be ideal threshold? Ideally we want to maximize both Sensitivity & Specificity. But this is not possible always

Description: Short published article on the basic concepts of sensitivity and specificity.. Link: Article pdf. Who created this resource: This is one of two articles published together in the British Medical Journal (BMJ) in 1994 by two very well-known British biostatisticians and educators, Douglas G. Altman (1948-2018), late professor of statistics in medicine at Oxford University, and J. In HIV tests, the sensitivity is important, but even more so is the specificity, i.e., the reliability of positive tests results. A third parameter, the positive predictive value, is connected with these test characteristics; but even more it depends to a high degree on the prevalence of the infection ** Then, when interpreting the ROC curve you want your classifiers to be positioned as close as possible to the top left corner indicating low false positive rate (high specificity) and high true positive rate (high sensitivity)**. With that said, the false positive rate doesn't represent the specificity, but the negative of the specificity instead

** The test's sensitivity measures how correctly it identifies those with the disease**. The same test will come back negative in about 95% of healthy people without celiac disease. The test's specificity refers to how accurately it is able to identify those without the disease. The tTG test is the most sensitive test for celiac disease The sensitivity and negative predictive value (NPV) of increased high-sensitivity cTnT (>14 pg/mL of the 99th percentile of a healthy population) were higher than those of increased H-FABP (≥6.2 ng/mL of the upper reference limit). In addition, there was no significant difference in specificity between high-sensitivity cTnT and H-FABP

- (*) These values are dependent on disease prevalence. Definitions. Sensitivity: probability that a test result will be positive when the disease is present (true positive rate). = a / (a+b) Specificity: probability that a test result will be negative when the disease is not present (true negative rate). = d / (c+d) Positive likelihood ratio: ratio between the probability of a positive test.
- Sensitivity and specificity are two statistical measures of test performance. The origins of these measures comes (unsurprisingly) from screening tests for diseases whereby the purpose of the test is to differentiate between those who do and do not have the disease (so that appropriate diagnosis and treatment can occur). The key thing here is to
- Can anyone explain
**how****to**calculate the accuracy,**sensitivity****and****specificity**of multi-class dataset? machine-learning confusion-matrix multiclass-classification. Share. Improve this question. Follow edited Dec 24 '20 at 22:38. desertnaut. 45.4k 18 18 gold badges 108 108 silver badges 138 138 bronze badges - One may also ask, what is specificity and sensitivity? In medical diagnosis, test sensitivity is the ability of a test to correctly identify those with the disease How do you interpret specificity? Sensitivity is the true positive rate, equivalent to a/a+c. Specificity is the true negative rate, equivalent to d/b+d
- The focus of sensitivity and specificity is on the test. Whereas the focus on PPV and NPV is on the patient. You could try re-stating them as follows: Sensitivity is the probability that the test is positive when the disease is present. Whereas PPV is the probability that the disease is present when the test is positive

Read CDC's interim guidelines for using antibody tests in clinical and public health settings. CDC's serologic test has a specificity of greater than 99% and a sensitivity of 96% based on performance evaluations. It can be used to identify past SARS-CoV-2 infection in people who were infected at least 1 to 3 weeks previously Usage Note 24170: Estimating sensitivity, specificity, positive and negative predictive values, and other statistics There are many common statistics defined for 2×2 tables. Some statistics are available in PROC FREQ Sensitivity and specificity are commonly used measures of the validity of a screening of a test (Aschengrau & Seage, pp. 421-422). Validity is the ability of a test to correctly categorize persons into their true disease status Therefore, understanding sensitivity, specificity, and how test performance is influenced by disease prevalence is important in any testing strategy. How sensitivity and specificity affect test manufacturing and use The higher the values of a test's sensitivity and specificity (each out of 100%), the more accurat

Sensitivity and specificity are proof that not every test is perfect. Some are better at ruling disease in, others are better at ruling things out. Either way, we need to appreciate the shortcomings of certain tests and make sure we do the appreciate tests when investigating symptoms and coming up with a diagnosis ** Sensitivity and specificity are the most widely used statistics used to describe a diagnostic test**. Unfortunately, as we learned from the example of interpreting a mammogram above, they are not very helpful to clinicians trying to revise the probability of disease. Reviewing the definitions of prevalence, sensitivity, and specificity will help.

When sensitivity decreases, the test's utility as a screening test is diminished because the test fails to identify asymptomatic patients . When specificity decreases, the test's utility as a screening test may diminish because it results in too many needless work-ups. 32 IF • Prevalence (prior probability) increase Sensitivity and specificity are essential indicators of test accuracy and allow healthcare providers to determine the appropriateness of the diagnostic tool. Providers should utilize diagnostic tests with the proper level of confidence in the results derived from known sensitivity, specificity, positive predictive values (PPV), negative. Two indices are used to evaluate the accuracy of a test that predicts dichotomous outcomes (e.g. logistic regression) - sensitivity and specificity. They describe how well a test discriminates between cases with and without a certain condition ** Antibody tests for SARS-CoV-2 are hard to interpret**. Update: As of May 4, the FDA will only issue emergency use authorizations to tests that have at least 90% sensitivity and 95% specificity

Culture and Sensitivity - C & S Culture: You send a specimen to the lab and the labs job is to tell you what the organism is, a definitive ID based on gram stain, morphology and biochemical profile. Susceptibility: The lab also gives you the info on the antibiotic susceptibilities to know how to treat it With the sensitivity and specificity of 90%, the test will correctly pick up 90%, or 45 individuals, with the disease, and 90%, or 855 individuals, without the disease. The remaining 5 will be classified as false negatives and 95 as false positives. Figure 4. Within a population of 1000 people, a disease with a 5% prevalence will impact 50 people

Sensitivity and specificity are two of them. In short: at a sensitivity of 100% everyone who is ill is correctly identified as being ill. At a specificity of 100% no one will get a false positive test result. Tests that score 100% in both areas are actually few and far between intervals, based on a specified sensitivity and specificity , interval width, confidence level, and prevalence. Caution: This procedure assumes that the sensitivity and specificity of the future sample will be the same as the sensitivity and specificity that is specified. If the sample sensitivity or specificity is different from the on

The MDQ characteristics (sensitivity, specificity) are held constant. Look at what happens to predictive values (positive and negative, respectively, in the right hand column) when the prevalence of the problem goes from low to high in Scenario A and then B **Sensitivity** **and** **specificity** are commonly used measures of the validity of a screening of a test (Aschengrau & Seage, pp. 421-422). Validity is the ability of a test to correctly categorize persons into their true disease status

Department of Pathology and Laboratory Medicine. Perelman School of Medicine at the University of Pennsylvania 3400 Spruce St. Philadelphia, PA 19104-423 ** Tradeoffs between sensitivity and specificity**. For most tests, if you increase sensitivity, specificity will drop. And vice versa. While it is possible to have a test that has both 100% sensitivity and 100% specificity, chances are that in those cases distinguishing between who has disease and who doesn't is so obvious that you didn't need the test in the first place

Sensitivity [edit | edit source] Sensitivity is defined as the ability of a test to identify patients with a particular disorder. In other words, it represents the proportion of a population with the target disorder that has a positive result with the diagnostic test. Tests that are highly sensitive are most useful for ruling out a disorder, as. Compared to CM, the sensitivity of FM was higher (72% vs. 64%, P = 0.005), and the specificity lower (81% vs. 96%, P < 0.001). In receiver operating characteristic analysis, maximum area under the curve for FM was obtained at a threshold of >4 acid-fast bacilli/100 fields (sensitivity 68%, specificity 90%)

Specificity, Sensitivity and the True Skill Statistic By Farzin Shabani, Lalit Kumar & Mohsen Ahmadi . University of New England Abstract- We aimed to assess different methods for evaluating performance accuracy in species distribution models based on the application of five types of bioclimatic models under thre Sensitivity and Specificity. a patients shoulder and been concerned of whether it was a pearsons analysis or t-something in the article I just read. However, the one part of statistics that is very important clinically is understanding specificity and sensitivity For any given test administered to a given population, it is important to calculate the sensitivity, specificity, positive predictive value, and negative predictive value, in order to determine how useful the test is to detect a disease or characteristic in the given population.If we want to use a test to test a specific characteristic in a sample population, we would like to know Sensitivity: 900/1000 = 90%. Specificity: 8550/9000 = 95%. Positive predict value: 900/1350 = 67% (up from 1.8%) Negative predict value: 8550/8650 = 98.8% (down from 99.99%) Likelihood ratios. We'd like the measure to be a feature of the test so it is stable across different prevalences/pretest probabilitie The Prostate Cancer Prevention Trial yields a means to evaluate PSA screening for prostate cancer detection. The receiver operating characteristic curve shows that PSA above 2.5 provides optimum sensitivity and specificity for prostate cancer diagnosis by needle biopsy. However, the maximum positive

Sensitivity and Specificity. Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed) Year introduced: 1991. PubMed search builder options. Subheadings Unlike sensitivity and specificity, which do not apply to specific patient probabilities, the LR allows clinicians to interpret test results in a specific patient provided there is a known (albeit often estimated) pre-test probability of disease My problem is when I get the classification table with probability level 0.5, the percentages of sensitivity and specificity are 0% and 100% respectively. I have tried with different prob. levels (0.4 and 0.3) with no luck. Searching on the internet I found that one possible reason is the rare of events The terms sensitivity, specificity, and positive/negative predictive values all refer the diagnostic utility of a certain test. It is important to remember that these are based on disease prevalence, which varies depending on the population being tested Specificity and Sensitivity of Indicators Long lists of indicators can present challenges for drawing inference about overall ecosystem status. A useful way to interpret lists of indicators in aggregate focuses on one of the primary considerations in the set of evaluation criteria introduced above, the indicator responds predictably and is.

Sensitivity and Specificity By changing the threshold, the good and bad customers classification will be changed hence the sensitivity and specificity will be changed. Which one of these two we should maximize? What should be ideal threshold In this lesson we will take a look at how good tests are at picking up the presence or absence of disease, helping us choose appropriate tests, and how to interpret positive and negative results. We'll decipher sensitivity, specificity, positive and negative predictive values

Once we request the Sensitivity Report, a new page will be generated in the Excel file in which we are working, with a report on the results. For the example proposed in this article, we get the following results: In the following section, we will go over how to interpret each of the three parts that the Sensitivity Report gives us to solve The sensitivity is zero (none of the true positives were detected = 100% false negative rate) but the specificity is 100% (you did not have any false positives - none of the not in class samples were marked in class) the concepts of sensitivity, specificity, NPV and positive pre-dictive value. SENSITIVITY The sensitivity of a test is defined as the proportion of people with disease who will have a positive result. If we apply Test Atoour hypothetical population, and 8 of the 10 people with Disease A test positive, then the sensitivity of the test is 8/10 or. The key difference between sensitivity and specificity is that sensitivity measures the probability of actual positives, while specificity measures the probability of actual negatives.. Sensitivity and specificity are two terms we come across in statistical testing. Depending on the nature of the study, the importance of the two may vary One may also ask, what is specificity and sensitivity? In medical diagnosis, test sensitivity is the ability of a test to correctly identify those with the disease (true positive rate), whereas test specificity is the ability of the test to correctly identify those without the disease (true negative rate)