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Following the analysis spectrum, analysts progress from facts to meaning. The journey generally starts with exploring and describing the available data and understanding what is available, what is missing, and what can be done with the data. From this phase, some stories will appear that will be further explored and confirmed by systematically comparing data and looking for relationships. Making solid arguments based on firm evidence and logic is at the heart of the analysis. After compiling and evaluating the relevant evidence, analysts need to formulate conclusions, recognize unsupported assumptions and consider alternative explanations. The spectrum can be tied to the expectations of analysis in the humanitarian Program Cycle, see image below to identify the level of analysis required as a response progresses.

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Biases and how to mitigate them

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Bias	Description	Examples
Selection bias	caused by choosing non-random data for analysis. Some information is unconsciously chosen or disregarded, misleading the analyst into a wrong conclusion.	Absence of evidence Anchoring Effect Availability Cascade Confirmation Bias Conservatism Bias Satisficing or Premature Closure	Evidence Acceptance Pro-Innovation Bias Publication Bias Recency Salience or Vividness Survivorship
	The key to overcoming selection biases is to examine carefully the credibility and reliability of sources and data use to base the analysis.
	Usability of Data Are they relevant to your research topic? Are they complete? Are they sufficiently recent? Are they sensitive? Are they representative? Are they comparable to other data you have available? Are they trustworthy?	Credibility of the Data: Evaluate how accurate and precise the information is Check for strong corroboration and consistency with other sources Look for negative cases Identify key themes indicated in the evidence Consider if the explanation is plausible given the context Re-examine previously dismissed information or evidence Consider whether ambiguous information has been interpreted and caveated properly Indicate the level of confidence in references	Reliability of Sources Qualifications and technical expertise of the source Reputation and track record for accuracy Its objectivity and motive for bias It's proximity to the original source or event
Bias	Description	Examples
Social Bias	the result of interactions with other people. The way we are processing and analyzing information depends on our relations with the persons who provided us with the information or hypothesis.	Institutional Bias Halo Effect Stereotyping Implicit Association	Attribution Error False Consensus Group Think Mirror Imaging (or Projection)
Mitigation techniques	The key to overcome social biases is to examine carefully the number of assumptions used to fill information gaps and to actively seek alternative hypothesis.
Mitigation techniques	Alternate Hypothesis Competing Hypothesis Devil’s Advocacy Differential Diagnosis Key Assumptions Checklist Logic Mapping	Challenging a view or consensus by building the best possible case for an alternative explanation and explicitly contesting key assumption to see if they will hold. Identify key assumptions Selection one or more assumptions that seem susceptible to challenge Review the evidence to determine if some are questionable validity Highlight any evidence that could support an alternative hypothesis or contracts the current thinking
Process	our tendency to process information based on cognitive factors rather than evidence. When we process information, we often display inherent thinking errors. They prevent an analyst from accurately understanding reality even when all the needed data and evidence are in his/her hands.	Blind Spot Overconfidence Choice-Supportive Clustering Illusion Hindsight Bias Irrational (Commitment) Escalation Selective Attention/Perception Information Volume Bias Framing	Hyperbolic Discounting Impact Negativity Ostrich Effect Planning Bias Status Quo Wishful Thinking Risk-averse Zero Risk
Bias	Description	Examples
Mitigation techniques	By making the way the information processed obvious to everyone, members of a team can acknowledge the limitations and advantages of each of the roles.
	Edward de Bono’s method is a parallel thinking process that helps analysts overcoming their assumption, biases, and heuristics. Members of a team are assigned with a “role” to play a hat to wear. Members of a team are assigned with a role to play, a hat to wear They can more easily examine a hypothesis from different angles: neutral, emotional, creative, optimist, and pessimist angle. Image RemovedImage Added	Structured analytical thinking can be used to overcome cognitive limitations and develop analytical objectivity. Image RemovedImage Added	the use of frameworks