Testing Assumptions - Revision history

Mmusij: /* See Also */

2018-12-09T22:47:06Z

See Also

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	* [[Empirical vs Predictive Control Processes]]		* [[Empirical vs Predictive Control Processes]]
	* [[Evolutionary Strategy]]		* [[Evolutionary Strategy]]
			* [[Customer Testing]]

	==References==		==References==
	# [http://theleanstartup.com/ http://theleanstartup.com/] accessed 4 July 2018		# [http://theleanstartup.com/ http://theleanstartup.com/] accessed 4 July 2018

Mmusij: /* See Also */

2018-12-09T22:45:58Z

See Also

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	==See Also==		==See Also==
	* [[Empirical vs Predictive Control Processes]]		* [[Empirical vs Predictive Control Processes]]
			* [[Evolutionary Strategy]]

	==References==		==References==
	# [http://theleanstartup.com/ http://theleanstartup.com/] accessed 4 July 2018		# [http://theleanstartup.com/ http://theleanstartup.com/] accessed 4 July 2018

Mmusij at 22:43, 9 December 2018

2018-12-09T22:43:50Z

← Older revision		Revision as of 22:43, 9 December 2018
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	Building quick prototypes, such as paper prototypes for example, can help to illustrate new ideas and approaches. But have to be able to be constructed quickly and only show the concepts as opposed to final features. (The previous section on Customer Research provides more examples and approaches to prototyping.)		Building quick prototypes, such as paper prototypes for example, can help to illustrate new ideas and approaches. But have to be able to be constructed quickly and only show the concepts as opposed to final features. (The previous section on Customer Research provides more examples and approaches to prototyping.)

			==Divergence vs. Convergence==
			In a project or product development’s timeline the most common approach is to start with an idea and then focus on refining the idea down to a defined solution sometimes referred to as Convergent Thinking. This is good to exploit and refine ideas, make critical choices and converge the solution with respect to a release or delivery date.

			[[File:Divergent_Convergence.jpg\|500px\|Divergence vs. Convergence]]

			In contrast there is also Divergent Thinking which focusses on exploring and searching in the solution space for possibilities. Solutions may be purposefully kept ambiguous in order to enable an appropriate idea to emerge from the perceived chaos and confusion.

			==Min Max Problem aka “False Peaks”==
			This is a common problem when trying to reach an optimum within a localised search space, in that, if the search space is too narrow or sufficient searching is not done, then a higher performing optimum peak solution may have been missed.

			[[File:Min_Max_Problem.jpg\|500px\|Min Max Problem]]

			When there is schedule pressure or a need to deliver quickly there can be a tendency to converge on a solution too early in order to make a release date or milestone. The overwhelming attraction to delivering overrides the ability to search and consider alternative solutions.

			The challenge for the Product Owner is to hold their nerve, become comfortable with the ambiguity and use the uncertainty at the start of the timeline to their advantage by commissioning a range of experiments and prototypes searching the solution space for better possibilities. This might be in the form of submitting a range of features for customer testing or trying a number of prototypes to evaluate technical possibilities. Those features or prototypes that score highly then become the basis for converging and refining towards an end solution.

			Jumping into Convergent Thinking too quickly and too early in the timeline can constrain the number of possibilities and limit the outcomes to only mediocre solutions. Hence, the Product Owner role should also focus on trying new ideas and experiments in the early Sprints in order to accommodate some Divergent Thinking, rather than just focussing on converging.

			==Bias==
			When empathising with a customer’s needs it is important to be aware of one’s own bias that may be influencing how concepts are being interpreted and new features devised.
			We all have our interpretation of the world around us and we can inadvertently influence or steer other people’s perspectives to match our own. This can often manifest as unconscious suggestions, critique or passing comments that filter thoughts, ideas and even the understanding of the data to match our own understanding.

			Being consciously aware of the impact that we have on others and the end product can make the difference between a product being received well by customers and failed ideas that sounded great in the meeting room, but which then fall flat when released into the world due to a misalignment of customer needs and solutions.

			===Mental Models===
			Mental models are the unwritten rules that we have made to help us make sense of the world around us. They may be based upon our past experiences and what we understand to be true.
			Whilst allowing us to manage the huge amounts of information that we deal with on a day to day basis, mental models can also impede our creativity and sense of openness. We might criticise ideas that don’t align to our own set of values and logical understanding for example.

			As a Product Owner it is important to recognise when your own mental models are at play and constraining the possibilities of what could be, and a conscious effort needs to be made to be more open instead.

			As well as the Product Owner having mental models and somewhat rigid perspectives, consumers and customers also have their own mental models. When introducing a new feature or product it is worth remembering that customers may not see what you see and that their own experiences are influencing their openness to new ideas.

			On the one hand customers may only perceive that they want more of what they already know, and discount new ideas that are counter to that experience. On the other, surprising customers with unexpected features that enhance or revolutionise the way they currently do things may be an excitement in the product or service that other providers have missed.
			Generally, humans are not linear or simple, or can be easily categorised. They are deeply complex, mysterious and independent beings.

			===Cognitive Bias===
			There are many different types of cognitive bias, generally it is the act of finding information to align and reaffirm our pre-defined conclusions.

			Data Bias for example, will encourage individuals to review charts and graphs, filtering out everything except the information that supports their existing understanding. This can be quite frustrating when the data has been carefully curated to be objective evidence, and yet a subjective “spin” on the data is verbalised and retained.

			The role of the Product Owner needs to be aware of their own cognitive bias and the cognitive bias of leaders and stakeholders, and try to draw attention to what the feedback or the data is actually telling them.

	==See Also==		==See Also==

Mmusij: /* References */

2018-12-09T22:38:57Z

References

← Older revision		Revision as of 22:38, 9 December 2018
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	==References==		==References==
			# [http://theleanstartup.com/ http://theleanstartup.com/] accessed 4 July 2018

Mmusij: /* Lean Startup with Sprints */

2018-12-09T22:38:33Z

Lean Startup with Sprints

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	==Lean Startup with Sprints==		==Lean Startup with Sprints==
	The Lean Startup movement[24] was initiated by Eric Ries and his book of the same name. The approach is much like using experiments, however, this approach conducts longer running experiments that may encompass a number of Sprints to build attributes to be tested and evaluated when used within a Scrum context.		The Lean Startup movement[1] was initiated by Eric Ries and his book of the same name. The approach is much like using experiments, however, this approach conducts longer running experiments that may encompass a number of Sprints to build attributes to be tested and evaluated when used within a Scrum context.

	The approach begins with a hypothesis or a question to be answered that are normally quite high level and challenging. The experiment then runs and may well include several Sprints as mini experiments to contribute to the overall hypothesis. Each Sprint will have their own Sprint Goals to position the Sprints, which may be sub parts of the overall hypothesis.		The approach begins with a hypothesis or a question to be answered that are normally quite high level and challenging. The experiment then runs and may well include several Sprints as mini experiments to contribute to the overall hypothesis. Each Sprint will have their own Sprint Goals to position the Sprints, which may be sub parts of the overall hypothesis.

Mmusij: /* Lean Startup with Sprints */

2018-12-09T22:37:59Z

Lean Startup with Sprints

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	The approach begins with a hypothesis or a question to be answered that are normally quite high level and challenging. The experiment then runs and may well include several Sprints as mini experiments to contribute to the overall hypothesis. Each Sprint will have their own Sprint Goals to position the Sprints, which may be sub parts of the overall hypothesis.		The approach begins with a hypothesis or a question to be answered that are normally quite high level and challenging. The experiment then runs and may well include several Sprints as mini experiments to contribute to the overall hypothesis. Each Sprint will have their own Sprint Goals to position the Sprints, which may be sub parts of the overall hypothesis.
	Towards the end of the experiment validated learning is collected, which may be through the results of customer testing, surveys and other data that helps to prove or disprove the hypothesis.		Towards the end of the experiment validated learning is collected, which may be through the results of customer testing, surveys and other data that helps to prove or disprove the hypothesis.

			[[File:The_Lean_Startup_Cycle.jpg\|500px\|Lean Startup]]

	Finally, the cycle ends with a decision to pivot and try a different hypothesis for the next experiment or persevere and continue to refine the hypothesis based upon the findings and observations. The cycle starts again with a new or refined hypothesis.		Finally, the cycle ends with a decision to pivot and try a different hypothesis for the next experiment or persevere and continue to refine the hypothesis based upon the findings and observations. The cycle starts again with a new or refined hypothesis.

Mmusij: /* See Also */

2018-12-09T22:34:33Z

See Also

← Older revision		Revision as of 22:34, 9 December 2018
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	==See Also==		==See Also==
	* [[Empirical vs Predictive ~~Process~~ Control]]		* [[Empirical vs Predictive Control Processes]]

	==References==		==References==

Mmusij: /* See Also */

2018-12-09T22:34:06Z

See Also

← Older revision		Revision as of 22:34, 9 December 2018
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	==See Also==		==See Also==
	* [[Predictive ~~vs. Empirical~~ Process Control]]		* [[Empirical vs Predictive Process Control]]

	==References==		==References==

Mmusij: /* See Also */

2018-12-09T22:33:36Z

See Also

← Older revision		Revision as of 22:33, 9 December 2018
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	==See Also==		==See Also==
	* [[Predictive vs. Empirical Control]]		* [[Predictive vs. Empirical Process Control]]

	==References==		==References==

Mmusij at 22:33, 9 December 2018

2018-12-09T22:33:25Z

← Older revision		Revision as of 22:33, 9 December 2018
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	==See Also==		==See Also==
			* [[Predictive vs. Empirical Control]]

	==References==		==References==