Probability of Failure given Odds against Solution

STEP 0: Pre-Calculation Summary
Formula Used
Probability of Failure = Odds Against/(Odds Against+1)
q = OA/(OA+1)
This formula uses 2 Variables
Variables Used
Probability of Failure - Probability of Failure is the likelihood of losing an event.
Odds Against - Odds Against is the ratio of the number of unfavorable outcomes (losing) to the number of favorable outcomes (winning).
STEP 1: Convert Input(s) to Base Unit
Odds Against: 0.67 --> No Conversion Required
STEP 2: Evaluate Formula
Substituting Input Values in Formula
q = OA/(OA+1) --> 0.67/(0.67+1)
Evaluating ... ...
q = 0.401197604790419
STEP 3: Convert Result to Output's Unit
0.401197604790419 --> No Conversion Required
FINAL ANSWER
0.401197604790419 0.401198 <-- Probability of Failure
(Calculation completed in 00.004 seconds)

Credits

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Created by Nayana Phulphagar
Institute of Chartered and Financial Analysts of India National college (ICFAI National College), HUBLI
Nayana Phulphagar has created this Calculator and 300+ more calculators!
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Verified by Nikita Kumari
The National Institute of Engineering (NIE), Mysuru
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18 Odds Probability Calculators

Probability of Success
​ Go Probability of Success in Binomial Distribution = Number of Wins/(Number of Wins+Number of Losses)
Odds in Favor given Probability of Success
​ Go Odds in Favor = Probability of Success in Binomial Distribution/(1-Probability of Success in Binomial Distribution)
Odds against given Probability of Success
​ Go Odds Against = (1-Probability of Success in Binomial Distribution)/Probability of Success in Binomial Distribution
Probability of Failure
​ Go Probability of Failure = Number of Losses/(Number of Wins+Number of Losses)
Odds in Favor given Probability of Success and Failure
​ Go Odds in Favor = Probability of Success in Binomial Distribution/Probability of Failure
Odds against given Probability of Success and Failure
​ Go Odds Against = Probability of Failure/Probability of Success in Binomial Distribution
Probability of Success given Odds in Favor
​ Go Probability of Success in Binomial Distribution = Odds in Favor/(Odds in Favor+1)
Odds in Favor given Probability of Failure
​ Go Odds in Favor = (1-Probability of Failure)/Probability of Failure
Odds against given Probability of Failure
​ Go Odds Against = Probability of Failure/(1-Probability of Failure)
Probability of Failure given Odds against
​ Go Probability of Failure = Odds Against/(Odds Against+1)
Odds in Favor
​ Go Odds in Favor = Number of Wins/Number of Losses
Odds against
​ Go Odds Against = Number of Losses/Number of Wins
Probability of Success given Probability of Failure
​ Go Probability of Success in Binomial Distribution = 1-Probability of Failure
Probability of Failure given Probability of Success
​ Go Probability of Failure = 1-Probability of Success in Binomial Distribution
Probability of Success given Odds against
​ Go Probability of Success in Binomial Distribution = 1/(Odds Against+1)
Probability of Failure given Odds in Favor
​ Go Probability of Failure = 1/(Odds in Favor+1)
Odds in Favor given Odds against
​ Go Odds in Favor = 1/Odds Against
Odds against given Odds in Favor
​ Go Odds Against = 1/Odds in Favor

Probability of Failure given Odds against Formula

Probability of Failure = Odds Against/(Odds Against+1)
q = OA/(OA+1)

What are Odds in Probability?

In probability theory, odds provide a measure of the likelihood of a particular outcome. They are calculated as the ratio of the number of events that produce that outcome to the number that do not. Odds are commonly used in gambling and statistics.

Odds also have a simple relation with probability: the odds of an outcome are the ratio of the probability that the outcome occurs to the probability that the outcome does not occur.

Odds can be expressed as a ratio of two numbers or as a number, by dividing the terms in the ratio.

Odds range from 0 to infinity, while probabilities range from 0 to 1, and hence are often represented as a percentage between 0% and 100%.

How to Calculate Probability of Failure given Odds against?

Probability of Failure given Odds against calculator uses Probability of Failure = Odds Against/(Odds Against+1) to calculate the Probability of Failure, The Probability of Failure given Odds against formula is defined as the likelihood that the outcome does not occur, calculated using odds against. Probability of Failure is denoted by q symbol.

How to calculate Probability of Failure given Odds against using this online calculator? To use this online calculator for Probability of Failure given Odds against, enter Odds Against (OA) and hit the calculate button. Here is how the Probability of Failure given Odds against calculation can be explained with given input values -> 0.401198 = 0.67/(0.67+1).

FAQ

What is Probability of Failure given Odds against?
The Probability of Failure given Odds against formula is defined as the likelihood that the outcome does not occur, calculated using odds against and is represented as q = OA/(OA+1) or Probability of Failure = Odds Against/(Odds Against+1). Odds Against is the ratio of the number of unfavorable outcomes (losing) to the number of favorable outcomes (winning).
How to calculate Probability of Failure given Odds against?
The Probability of Failure given Odds against formula is defined as the likelihood that the outcome does not occur, calculated using odds against is calculated using Probability of Failure = Odds Against/(Odds Against+1). To calculate Probability of Failure given Odds against, you need Odds Against (OA). With our tool, you need to enter the respective value for Odds Against and hit the calculate button. You can also select the units (if any) for Input(s) and the Output as well.
How many ways are there to calculate Probability of Failure?
In this formula, Probability of Failure uses Odds Against. We can use 3 other way(s) to calculate the same, which is/are as follows -
  • Probability of Failure = 1/(Odds in Favor+1)
  • Probability of Failure = Number of Losses/(Number of Wins+Number of Losses)
  • Probability of Failure = 1-Probability of Success in Binomial Distribution
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