Biases or cognitive biases are irrational judgments or subconscious inferences made from the data available to us.
In testing, biases have the effect of causing you to miss or focus too much on specific behavior, processes, or data. We use our knowledge of biases in testing to improve our strategies by using biases to our advantage.
Biases can be used as a heuristic, for example, in learning about our team's desires and expectations. Ignoring biases can affect your perception of the product you're testing and the quality of your testing.
It may lead to gaps in your testing, and bugs could slip through. You need to be aware when you're using a bias deliberately that there is potential information you're missing out on, and you need to carry out additional activities to balance it out. Being conscious of biases allows us to attempt to prevent them from negatively impacting our testing.
We could also use biases to focus or zone in on specific testing activities. Example biases: Inattentional blindness, when you miss something in one area of the application because you're focused on another point.
Confirmation bias, when you promote data that proves your point of view and ignore data that challenges it. Observational bias. So the expectancy what that what we see, what we want to see, or in testing, create tests to return what we want to see.
In testing, biases have the effect of causing you to miss or focus too much on specific behavior, processes, or data. We use our knowledge of biases in testing to improve our strategies by using biases to our advantage.
Biases can be used as a heuristic, for example, in learning about our team's desires and expectations. Ignoring biases can affect your perception of the product you're testing and the quality of your testing.
It may lead to gaps in your testing, and bugs could slip through. You need to be aware when you're using a bias deliberately that there is potential information you're missing out on, and you need to carry out additional activities to balance it out. Being conscious of biases allows us to attempt to prevent them from negatively impacting our testing.
We could also use biases to focus or zone in on specific testing activities. Example biases: Inattentional blindness, when you miss something in one area of the application because you're focused on another point.
Confirmation bias, when you promote data that proves your point of view and ignore data that challenges it. Observational bias. So the expectancy what that what we see, what we want to see, or in testing, create tests to return what we want to see.