By Marcus Coetzee, 18 June 2021.

1. Introduction

Strategy emerges from how we think about the complex problems facing our organizations. These problems might relate to our environment, the challenges faced by our beneficiaries or something inside our organization. To become better at developing strategies, we must learn how to think more clearly and avoid cognitive biases.

My ability to think strategically has benefited immensely from understanding the differences between inductive and deductive reasoning, and understanding when and how to apply them. Inductive reasoning involves ‘bottom up thinking’ – constructing theories from details. In contrast, deductive reasoning involves ‘top down thinking’ – starting with a theory and assuming details that must be true if the theory is valid.

We all have our preferences for one of these types of reasoning when solving complex problems that affect organizations and communities. Nevertheless, it is beneficial to master both types of reasoning so that we can use them when the need arises.

This article summarizes what I have learned so far while diving into this topic. It is a detailed and technical article that will interest people who want to enhance how they use reasoning to solve problems.

2. Terminology

Here is some of the terminology I use in this article:

‘Theories’ include beliefs, principles, generalizations, rules, patterns, conjectures and conclusions that describe a part of the world that is greater than what was observed. These theories are used to explain or predict that which was not observed or not yet observed.

‘Observations’ include experiences, cases and instances.

‘Hypotheses’ are clear statements that are the building blocks for theories. For example, it was raining this morning when I left my apartment. I hypothesized that drops of water would fall on me when I went outdoors. This hypothesis is a core component of our theory of rain.

‘Scientific’ is when we use inductive or deductive reasoning in a way that conforms with the standards prescribed by the philosophy of science, which explores the nature of scientific theories and methodologies. For example, the Principle of Falsification requires that a scientific theory is able to be disproved and should specify how this might be done.

3. Inductive reasoning

In this section I will introduce inductive reasoning and provide several examples. I will explain how inductive reasoning is intrinsically constrained by the need to make generalizations. I will also explain when and when not to use inductive reasoning. 

This section closes with a detailed example of how I used inductive reasoning to infer an informal theory that homelessness has increased in South Africa as a result of the Covid-19 pandemic and is unlikely to be alleviated any time soon.

3.1 What is inductive reasoning?

Inductive reasoning is commonly referred to as ‘bottom up’ thinking. It involves using details to infer theories that cover more than what was observed – i.e. creating generalizations based upon a set of observations. The statement of probable truth that we reach through inductive reasoning is sometimes called a ‘conjecture’.

The flowchart below illustrates the process of inductive reasoning.

We use inductive reasoning in our lives everyday to make sense of the world. Many of the theories we formulate are not scientific or academic but rather personal.

People are more likely to consider the theories that they develop through inductive reasoning to be true if their theories are associated with intense emotions, and if their repeated and different types of observations fit their theory. For example, someone will be more inclined to believe that their community is unsafe if they are a victim of crime, and if they know other people who have had similar experiences, and if they hear stories about their dangerous community on the radio.

In contrast, when inductive reasoning is used formally in statistics and quantitative research, then the strength of the resulting theory depends primarily on the sample design and research methodology. Let us assume that the researchers have a sample frame (with the details of the population that is being studied), and are able to draw a probability sample (where there is a positive and known chance of everyone being included in the sample). This would enable them to specify the exact statistical probability that their theory will apply to people, things and events that were not observed but are in the ambit of the theory.

3.2. Three examples of inductive reasoning

The best way to understand inductive reasoning is to see examples of how it is being used. Here are three that were on my mind when I wrote this article.

The first example relates to the National Income Dynamics Study – Coronavirus Rapid Mobile Survey (NIDS-CRAM). Enumerators phoned a nationally representative sample of South Africans during ‘hard lockdown’ to understand their social and economic circumstances. This yielded many insights about how South Africans were struggling with the symptoms of poverty such as a shortage of food and access to social services. This is an example of inductive reasoning because the detailed results of the interviews were used to create a broader theory about the socio-economic circumstances of all South Africans.

The second example relates to the stories of government corruption and ‘state capture’ that have filled the South African news cycle for several years. Investigative journalists and the Zondo Commission of Inquiry into Allegations of State Capture have uncovered many instances of large-scale corruption. Many South Africans, including myself, have inferred a theory about the nature and incidence of corruption in government and state-owned enterprises. Then when I heard of a massive tender (approx USD 15 billion) being awarded on short notice for the supply of electricity, I predicted that government corruption is most likely involved in the tender process. Time is revealing the truth of the matter. This is inductive reasoning because I used several observations about corruption to notice patterns and develop a personal theory about government corruption, from which I make informal predictions.

The third example relates to a project I’m currently working on in East Africa. I am part of a team that is working on a study of non-tariff barriers in the East African Community. We are gathering official statistics on trade in the region, as well as information from traders, transporters, clearing agents and border officials. There are several data gathering methodologies involved. We will primarily use inductive reasoning to assimilate this data and infer a theory about the negative impact of these trade barriers on the region and how best to mitigate them. This is inductive reasoning because we use a multitude of observations to develop a theory about how non-tariff barriers are affecting all trade in the region.

3.3. Inductive theories vary in their probability that they will apply to things that were yet not observed

The Problem of Induction was described by the philosopher David Hume in the 18th century. He explained why generalizing a set of observations can never be true – at the most they can be described as highly probable. This is the inherent risk that we all experience when making generalizations about a broader group or set of phenomena. However, this should not belittle the value of inductive reasoning since our mental models rely on this process. We should simply accept that the ‘map is not the territory’.

When conducting scientific research, it may be possible to specify the probability that the theory is true for observations that were not used to build the theory (i.e. for other people or future events that are not yet observed). 

When we cannot specify the probability that an inductive reasoning is true, the proponents of the theory must be transparent about the process and compromises with data and methodology that were required along the way. This enables others to judge for themselves how probable they believe the theory to be.

3.4. When to use inductive reasoning

Inductive reasoning is useful when you want to develop a general theory based upon a limited set of observations because you don’t have the means to investigate or measure everything.

It is also useful when you already understand the conceptual areas that you want to explore but want to understand the likely incidence or frequency that certain things are true. For example, I spent three years working on a study to assess the likelihood that certain demographic and background factors were associated with students dropping out of South African universities.

It can also be useful when you want to investigate the strength of relationships between things and the extent to which certain variables correlate with each other.

Finally, inductive reasoning is useful when you want to make a prediction about the future based on historical trends (e.g. unemployment rate and types of skills that the economy will need.)

3.5. Inductive reasoning needs the right data to work effectively

Flawed and improbable theories are created when we take data from one situation and generalize it to other situations that are very different from the one where the original data was obtained. The problem here is not so much with inductive reasoning per se, but rather with its poor use. This might involve:

• Attempting to generalize findings from one group to another with different characteristics. For example, a group of policy-makers might attempt to use a set of observations about the challenges faced by informal businesses in the Khayelitsha township in Cape Town to develop a theory about the challenges faced by all businesses in South Africa, regardless of their context or size. This resulting theory is likely to have some flaws.
• Attempting to generalize findings to different contexts. For example, mosquito nets that have been treated with insecticide have proven effective in randomized control trials at reducing the incidence of malaria in Africa with no harmful side effects. However, when these nets were given to certain fishing communities in Zambia, it was discovered  that these fishermen were using them to filter fish and other insects from rivers, lakes and wetlands which then damaged these ecosystems as an unintended consequence.
• Attempting to use associations between things to assume a causal relationship. For example, we know that high levels of vitamin D are associated with reduced Covid-19 symptoms, but this does not necessarily mean that taking vitamin D supplements will achieve the same since there might be other factors at play. People with ill-health or who are too sick to go outdoors will tend to have poor vitamin D levels.

The quality of the theories developed using inductive reasoning are also influenced by the quality of our mental models. For example, believers in the QAnon conspiracy have assimilated a disparate set of observations into a theory that a bunch of satanic power-hungry pedophiles are trying to take over the United States government.

I believe that we must learn to guard against theories where inductive reasoning has been used incorrectly since they can easily be used for nefarious purposes, or at the very least, these theories will mislead or misinform us.

3.6. Detailed example of inductive reasoning

While writing this article, I audited my belief that the social problem of homelessness has increased in South Africa as a result of the Covid-19 pandemic and is unlikely to be alleviated any time soon. The following flowchart shows a simplified version of how I unconsciously used inductive reasoning to infer this theory. You must read the flowchart from left to right.

Because this theory was developed informally and largely unconsciously, I can’t specify the probability that it is true for other neighborhoods in Cape Town and for other cities in South Africa. Neither am I an expert in homelessness. Nevertheless, I will refine my personal theory as I learn more about this problem and how it has recently worsened. 

4. Deductive reasoning

This section introduces deductive reasoning and provides several examples to show how it is different from inductive reasoning. I will explain when to use it and when not to use it. The section will conclude with a detailed example of how I might use deductive reasoning to develop a theory about the financial problems facing a non-profit organization.

4.1 What is deductive reasoning?

Deductive reasoning is commonly referred to as ‘top-down’ thinking. It involves adopting a theory, which was most likely developed using inductive reasoning, and then deducing details that must be true if the theory is valid.

The flowchart below illustrates the process of deductive reasoning.

Deductive thinking is closely associated with an experimental approach in science and academia. It is a straightforward method for checking the validity of the theory and then refining or discarding it. 

The Theory of Falsifiability by Karl Popper is pertinent as it states that a scientific theory is one that is capable of being disproved, and is valid until one of its hypotheses are proven to be false.

4.2. Three examples of deductive reasoning

Here are three examples of deductive reasoning that I have encountered in my work.

The first example relates to the Theory of Change, which is part of the doctrine of non-profit organizations and social enterprises. It starts with a theory about the end-state that must be achieved (i.e. the vision) and broadly how this can happen (i.e. mission). Deductive reasoning is then used to work backwards from the vision and map the key activities, outputs and outcomes that will achieve this end-state. A Theory of Change uses deductive reasoning because it starts with a theory of what can be achieved and deduces hypotheses that must be true for it to be valid.

The second example relates to the strategic work that I have done with the association of hospices in South Africa. During this time, we developed a theory using inductive reasoning about how the private sector will start to compete with traditional hospices, and how we should respond. Then we used deductive reasoning to deduce that private commercial hospices will seek to dominate the profitable market segments as soon as medical aid schemes pay properly for palliative care. This would present a threat to hospices since patients with medical aids cross-subsidize the services that hospices provide to poor communities. There is also the risk that hospices will consequently receive fewer bequests than before. Emerging evidence suggests that this hypothesis is true as some businesses have recently entered this market and begun to sell their services. Therefore our theory remains valid for now. This uses deductive reasoning since we started with the theory about competition from the private sector and unpacked the details of what must happen for our theory to hold ground.

The third example relates to randomized control trials (RCTs) which are based on deductive reasoning since they create testable hypotheses. Researchers then seek to falsify/disprove these hypotheses in order to test the validity of their theories that a certain type of intervention would produce a specific type of change. Examples of RCTs include:

• testing the efficacy of Covid vaccines
• testing whether marketing or financial training provides the greatest benefits for entrepreneurs
• testing whether money for mobile airtime and data, and travel subsidies can help young people to find work
• A/B testing by Instagram to test whether new features increase user engagement.

4.3. The best times to use deductive reasoning

The best time to use deductive reasoning is when there are diminishing returns to gathering more information using the inductive approach – i.e. as the new information adds few insights to what is already known. It is also useful when you are trying to understand the key drivers/causes of a problem or solution as opposed to things that are associated with it.

4.5. Common mistakes when using deductive reasoning

There are four common mistakes that I have noticed people make when using deductive reasoning to solve complex social problems. 

The first mistake is when one attempts to prove the validity of a theory by testing hypotheses that are not logically (or only partially related) to the theory. For example, let us assume that we were testing the market demand for a social enterprise that sells fortified food to feeding schemes and humanitarian agencies. A false hypothesis might be that ‘these potential customers have big annual budgets’ since this alludes to their ability to afford the food. However, I would argue that this would be a poor hypothesis since a big organizational budget does not necessarily mean that they spend a lot of money on food. Neither does it mean that they will want to buy the type of food that the social enterprise sells.

The second mistake is when one attempts to prove a theory by testing hypotheses that are not mutually exclusive (see MECE principle) since it would be difficult to isolate which of the hypotheses are true. For example, let us assume that your organization runs a diversion programme to rehabilitate young offenders and is trying to understand the efficacy of its activities. It would be poor practice to compare the effectiveness of its counseling programmes on young people versus unemployed people since these categories may overlap. Similarly, it would be unwise to hypothesize that a diversion programme and a counseling programme would be required to rehabilitate these youth since counseling is an integral part of diversion.

The third mistake is when one uses hypotheses where it is impossible to gather evidence to prove or disprove them. For example, a small non-profit organization that runs drama workshops in communities should be cautious about hypothesizing that they improve community cohesion.

The final mistake is when one tries to create an initial theory when insufficient information exists in the first place, and when inductive reasoning should first be used.

4.6. Detailed example of deductive reasoning

For this example, let us assume that a large non-profit organization needs our help with a formal assessment of some pressing problems that threaten its existence.

Then let us assume, that after some initial conversations and after reviewing some documents, we used inductive reasoning to develop a theory that the organization is struggling financially and at risk of running itself into the ground.

The following flowchart gives an example of the types of hypotheses that we might deduce from this theory.

Now that we have deduced some hypotheses, we should be able to identify the type of evidence that we need to determine which of these sub hypotheses are true. For example, let’s look at the evidence and actions that we might need to prove/disprove hypothesis 1.1 (‘the organization is in debt’). We might need to do the following:

• Review the balance sheet in the audited financial statements for the past three years and in the latest unaudited statement or management accounts.
• Calculate the debt and current ratios over the past three financial years.
• Review the components of current liabilities and long-term liabilities.
• Review a list of trade creditors.

We might discover that some of our hypotheses are valid and others are invalid. For example, Hypothesis 1.1 (‘The organization is in debt’) might be currently be invalid while Hypothesis 1.2 (‘financial reserves are deteriorating’) might be valid. 

Next we could use this feedback to refine our hypotheses and original ideas, and write them as follows:

• Hypothesis 1.1 – The organization’s assets are declining and the ratio between assets and liabilities is deteriorating overall.
• Hypothesis 1.2 – Financial reserves are deteriorating and being used to fund the shortfall in the budget and pay creditors, and will only last 12 months at the current rate of consumption. 

Then the hypothesis tree comes together. If all the evidence supports the hypotheses, then our theory that ‘the organization is struggling financially and is at risk of running itself into the ground’ would be sound. This deductive approach would also reveal some of the causes of the problem that would need to be addressed and make it easier to present our findings to the board of directors.

5. Conclusion

We use inductive and deductive reasoning all the time in our lives and work. We use it both formally and informally. The strategy, policy and research that we see around us is underpinned by one of these forms of reasoning, and possibly both.

This article has explained the differences in inductive and deductive reasoning. The former seeks to assimilate observations to develop probable theories to describe the unknown or predict the future, whereas the latter seeks to test the soundness of theories by using evidence to validate hypotheses. Both forms of reasoning are equally important. They work together to provide us with useful theories. They have enabled the human race to be as successful as it is.

However, we should be mindful of the limitations of these two types of reasoning. When used incorrectly, they can result in improbable or unsound theories that can limit our options and distort our thinking. They can also be used nefariously to promote flawed theories for a political or geopolitical agenda.

We should also strive to be able to use inductive and deductive reasoning more explicitly when required. I believe there is immense value in learning how to improve our reasoning – the purpose of this article. It will improve our ability to understand this complex world we live in and make much better decisions.

6. Further reading

Here are some of the links that were the most useful in researching this topic.

• Crafting Cases: The Definitive Guide to Issue Trees by Bruno Nogueira.
• Deductive vs Inductive Reasoning: Make Smarter Arguments, Better Decisions, and Stronger Conclusions posted on FS Blog 
• The McKinsey Way by Ethan Raisel (book)
• The Pyramid Principle: Logic in Writing and Thinking (book)