What is Hypothesis Generation?
Hypothesis generation involves making informed guesses about various aspects of a business, market, or problem that need further exploration and testing. It's a crucial step while applying the scientific method to business analysis and decision-making.
Here is an example from a popular B-school marketing case study:
A bicycle manufacturer noticed that their sales had dropped significantly in 2002 compared to the previous year. The team investigating the reasons for this had many hypotheses. One of them was: “many cycling enthusiasts have switched to walking with their iPods plugged in.” The Apple iPod was launched in late 2001 and was an immediate hit among young consumers. Data collected manually by the team seemed to show that the geographies around Apple stores had indeed shown a sales decline.
Traditionally, hypothesis generation is time-consuming and labour-intensive. However, the advent of Large Language Models (LLMs) and Generative AI (GenAI) tools has transformed the practice altogether. These AI tools can rapidly process extensive datasets, quickly identifying patterns, correlations, and insights that might have even slipped human eyes, thus streamlining the stages of hypothesis generation.
These tools have also revolutionised experimentation by optimising test designs, reducing resource-intensive processes, and delivering faster results. LLMs' role in hypothesis generation goes beyond mere assistance, bringing innovation and easy, data-driven decision-making to businesses.
Hypotheses come in various types, such as simple, complex, null, alternative, logical, statistical, or empirical. These categories are defined based on the relationships between the variables involved and the type of evidence required for testing them. In this article, we aim to demystify hypothesis generation. We will explore the role of LLMs in this process and outline the general steps involved, highlighting why it is a valuable tool in your arsenal.
Understanding Hypothesis Generation
A hypothesis is born from a set of underlying assumptions and a prediction of how those assumptions are anticipated to unfold in a given context. Essentially, it's an educated, articulated guess that forms the basis for action and outcome assessment.
A hypothesis is a declarative statement that has not yet been proven true. Based on past scholarship, we could sum it up as the following:
- A definite statement, not a question
- Based on observations and knowledge
- Testable and can be proven wrong
- Predicts the anticipated results clearly
- Contains a dependent and an independent variable where the dependent variable is the phenomenon being explained and the independent variable does the explaining
In a business setting, hypothesis generation becomes essential when people are made to explain their assumptions. This clarity from hypothesis to expected outcome is crucial, as it allows people to acknowledge a failed hypothesis if it does not provide the intended result. Promoting such a culture of effective hypothesising can lead to more thoughtful actions and a deeper understanding of outcomes. Failures become just another step on the way to success, and success brings more success.
Hypothesis generation is a continuous process where you start with an educated guess and refine it as you gather more information. You form a hypothesis based on what you know or observe.
Say you're a pen maker whose sales are down. You look at what you know:
- I can see that pen sales for my brand are down in May and June.
- I also know that schools are closed in May and June and that schoolchildren use a lot of pens.
- I hypothesise that my sales are down because school children are not using pens in May and June, and thus not buying newer ones.
The next step is to collect and analyse data to test this hypothesis, like tracking sales before and after school vacations. As you gather more data and insights, your hypothesis may evolve. You might discover that your hypothesis only holds in certain markets but not others, leading to a more refined hypothesis.
Once your hypothesis is proven correct, there are many actions you may take - (a) reduce supply in these months (b) reduce the price so that sales pick up (c) release a limited supply of novelty pens, and so on.
Once you decide on your action, you will further monitor the data to see if your actions are working. This iterative cycle of formulating, testing, and refining hypotheses - and using insights in decision-making - is vital in making impactful decisions and solving complex problems in various fields, from business to scientific research.
How do Analysts generate Hypotheses? Why is it iterative?
A typical human working towards a hypothesis would start with:
1. Picking the Default Action
2. Determining the Alternative Action
3. Figuring out the Null Hypothesis (H0)
4. Inverting the Null Hypothesis to get the Alternate Hypothesis (H1)
5. Hypothesis Testing
The default action is what you would naturally do, regardless of any hypothesis or in a case where you get no further information. The alternative action is the opposite of your default action.
The null hypothesis, or H0, is what brings about your default action. The alternative hypothesis (H1) is essentially the negation of H0.
For example, suppose you are tasked with analysing a highway tollgate data (timestamp, vehicle number, toll amount) to see if a raise in tollgate rates will increase revenue or cause a volume drop. Following the above steps, we can determine:
Now, we can start looking at past data of tollgate traffic in and around rate increases for different tollgates. Some data might be irrelevant. For example, some tollgates might be much cheaper so customers might not have cared about an increase. Or, some tollgates are next to a large city, and customers have no choice but to pay.
Ultimately, you are looking for the level of significance between traffic and rates for comparable tollgates. Significance is often noted as its P-value or probability value. P-value is a way to measure how surprising your test results are, assuming that your H0 holds true.
The lower the p-value, the more convincing your data is to change your default action.
Usually, a p-value that is less than 0.05 is considered to be statistically significant, meaning there is a need to change your null hypothesis and reject your default action. In our example, a low p-value would suggest that a 10% increase in the toll rate causes a significant dip in traffic (>3%). Thus, it is better if we keep our rates as is if we want to maintain revenue.
In other examples, where one has to explore the significance of different variables, we might find that some variables are not correlated at all. In general, hypothesis generation is an iterative process - you keep looking for data and keep considering whether that data convinces you to change your default action.
Internal and External Data
Hypothesis generation feeds on data. Data can be internal or external. In businesses, internal data is produced by company owned systems (areas such as operations, maintenance, personnel, finance, etc). External data comes from outside the company (customer data, competitor data, and so on).
Let’s consider a real-life hypothesis generated from internal data:
Multinational company Johnson & Johnson was looking to enhance employee performance and retention.
Initially, they favoured experienced industry candidates for recruitment, assuming they'd stay longer and contribute faster. However, HR and the people analytics team at J&J hypothesised that recent college graduates outlast experienced hires and perform equally well.
They compiled data on 47,000 employees to test the hypothesis and, based on it, Johnson & Johnson increased hires of new graduates by 20%, leading to reduced turnover with consistent performance.
For an analyst (or an AI assistant), external data is often hard to source - it may not be available as organised datasets (or reports), or it may be expensive to acquire. Teams might have to collect new data from surveys, questionnaires, customer feedback and more.
Further, there is the problem of context. Suppose an analyst is looking at the dynamic pricing of hotels offered on his company’s platform in a particular geography. Suppose further that the analyst has no context of the geography, the reasons people visit the locality, or of local alternatives; then the analyst will have to learn additional context to start making hypotheses to test.
Internal data, of course, is internal, meaning access is already guaranteed. However, this probably adds up to staggering volumes of data.
Looking Back, and Looking Forward
Data analysts often have to generate hypotheses retrospectively, where they formulate and evaluate H0 and H1 based on past data. For the sake of this article, let's call it retrospective hypothesis generation.
Alternatively, a prospective approach to hypothesis generation could be one where hypotheses are formulated before data collection or before a particular event or change is implemented.
A pen seller has a hypothesis that during the lean periods of summer, when schools are closed, a Buy One Get One (BOGO) campaign will lead to a 100% sales recovery because customers will buy pens in advance. He then collects feedback from customers in the form of a survey and also implements a BOGO campaign in a single territory to see whether his hypothesis is correct, or not.
The HR head of a multi-office employer realises that some of the company’s offices have been providing snacks at 4:30 PM in the common area, and the rest have not. He has a hunch that these offices have higher productivity. The leader asks the company’s data science team to look at employee productivity data and the employee location data. “Am I correct, and to what extent?”, he asks.
These examples also reflect another nuance, in which the data is collected differently:
- Observational: Observational testing happens when researchers observe a sample population and collect data as it occurs without intervention. The data for the snacks vs productivity hypothesis was observational.
- Experimental: In experimental testing, the sample is divided into multiple groups, with one control group. The test for the non-control groups will be varied to determine how the data collected differs from that of the control group. The data collected by the pen seller in the single territory experiment was experimental.
Such data-backed insights are a valuable resource for businesses because they allow for more informed decision-making, leading to the company's overall growth. Taking a data-driven decision, from forming a hypothesis to updating and validating it across iterations, to taking action based on your insights reduces guesswork, minimises risks, and guides businesses towards strategies that are more likely to succeed.
How can GenAI help in Hypothesis Generation?
Of course, hypothesis generation is not always straightforward. Understanding the earlier examples is easy for us because we're already inundated with context. But, in a situation where an analyst has no domain knowledge, suddenly, hypothesis generation becomes a tedious and challenging process.
AI, particularly high-capacity, robust tools such as LLMs, have radically changed how we process and analyse large volumes of data. With its help, we can sift through massive datasets with precision and speed, regardless of context, whether it's customer behaviour, financial trends, medical records, or more. Generative AI, including LLMs, are trained on diverse text data, enabling them to comprehend and process various topics.
Now, imagine an AI assistant helping you with hypothesis generation. LLMs are not born with context. Instead, they are trained upon vast amounts of data, enabling them to develop context in a completely unfamiliar environment. This skill is instrumental when adopting a more exploratory approach to hypothesis generation. For example, the HR leader from earlier could simply ask an LLM tool: “Can you look at this employee productivity data and find cohorts of high-productivity and see if they correlate to any other employee data like location, pedigree, years of service, marital status, etc?”
For an LLM-based tool to be useful, it requires a few things:
- Domain Knowledge: A human could take months to years to acclimatise to a particular field fully, but LLMs, when fed extensive information and utilising Natural Language Processing (NLP), can familiarise themselves in a very short time.
- Explainability: Explainability is its ability to explain its thought process and output to cease being a "black box".
- Customisation: For consistent improvement, contextual AI must allow tweaks, allowing users to change its behaviour to meet their expectations. Human intervention and validation is a necessary step in adoptingAI tools. NLP allows these tools to discern context within textual data, meaning it can read, categorise, and analyse data with unimaginable speed. LLMs, thus, can quickly develop contextual understanding and generate human-like text while processing vast amounts of unstructured data, making it easier for businesses and researchers to organise and utilise data effectively.LLMs have the potential to become indispensable tools for businesses. The future rests on AI tools that harness the powers of LLMs and NLP to deliver actionable insights, mitigate risks, inform decision-making, predict future trends, and drive business transformation across various sectors.
Together, these technologies empower data analysts to unravel hidden insights within their data. For our pen maker, for example, an AI tool could aid data analytics. It can look through historical data to track when sales peaked or go through sales data to identify the pens that sold the most. It can refine a hypothesis across iterations, just as a human analyst would. It can even be used to brainstorm other hypotheses. Consider the situation where you ask the LLM, "Where do I sell the most pens?". It will go through all of the data you have made available - places where you sell pens, the number of pens you sold - to return the answer. Now, if we were to do this on our own, even if we were particularly meticulous about keeping records, it would take us at least five to ten minutes, that too, IF we know how to query a database and extract the needed information. If we don't, there's the added effort required to find and train such a person. An AI assistant, on the other hand, could share the answer with us in mere seconds. Its finely-honed talents in sorting through data, identifying patterns, refining hypotheses iteratively, and generating data-backed insights enhance problem-solving and decision-making, supercharging our business model.
Top-Down and Bottom-Up Hypothesis Generation
As we discussed earlier, every hypothesis begins with a default action that determines your initial hypotheses and all your subsequent data collection. You look at data and a LOT of data. The significance of your data is dependent on the effect and the relevance it has to your default action. This would be a top-down approach to hypothesis generation.
There is also the bottom-up method, where you start by going through your data and figuring out if there are any interesting correlations that you could leverage better. This method is usually not as focused as the earlier approach and, as a result, involves even more data collection, processing, and analysis. AI is a stellar tool for Exploratory Data Analysis (EDA). Wading through swathes of data to highlight trends, patterns, gaps, opportunities, errors, and concerns is hardly a challenge for an AI tool equipped with NLP and powered by LLMs.
EDA can help with:
- Cleaning your data
- Understanding your variables
- Analysing relationships between variables
An AI assistant performing EDA can help you review your data, remove redundant data points, identify errors, note relationships, and more. All of this ensures ease, efficiency, and, best of all, speed for your data analysts.
Good hypotheses are extremely difficult to generate. They are nuanced and, without necessary context, almost impossible to ascertain in a top-down approach. On the other hand, an AI tool adopting an exploratory approach is swift, easily running through available data - internal and external.
If you want to rearrange how your LLM looks at your data, you can also do that. Changing the weight you assign to the various events and categories in your data is a simple process. That’s why LLMs are a great tool in hypothesis generation - analysts can tailor them to their specific use cases.
Ethical Considerations and Challenges
There are numerous reasons why you should adopt AI tools into your hypothesis generation process. But why are they still not as popular as they should be?
Some worry that AI tools can inadvertently pick up human biases through the data it is fed. Others fear AI and raise privacy and trust concerns. Data quality and ability are also often questioned. Since LLMs and Generative AI are developing technologies, such issues are bound to be, but these are all obstacles researchers are earnestly tackling.
One oft-raised complaint against LLM tools (like OpenAI's ChatGPT) is that they 'fill in' gaps in knowledge, providing information where there is none, thus giving inaccurate, embellished, or outright wrong answers; this tendency to "hallucinate" was a major cause for concern. But, to combat this phenomenon, newer AI tools have started providing citations with the insights they offer so that their answers become verifiable. Human validation is an essential step in interpreting AI-generated hypotheses and queries in general. This is why we need a collaboration between the intelligent and artificially intelligent mind to ensure optimised performance.
Clearly, hypothesis generation is an immensely time-consuming activity. But AI can take care of all these steps for you. From helping you figure out your default action, determining all the major research questions, initial hypotheses and alternative actions, and exhaustively weeding through your data to collect all relevant points, AI can help make your analysts' jobs easier. It can take any approach - prospective, retrospective, exploratory, top-down, bottom-up, etc. Furthermore, with LLMs, your structured and unstructured data are taken care of, meaning no more worries about messy data! With the wonders of human intuition and the ease and reliability of Generative AI and Large Language Models, you can speed up and refine your process of hypothesis generation based on feedback and new data to provide the best assistance to your business.