Signals in the Noise

After showing people a lot of data, and nattering on about various statistical models and results, I usually get asked a final question: “What does this mean?

People want solutions and accurate advice, not fancy models and numerical hedging.

The statistician in me wants to say: “It means we will reject the null hypothesis.”  The manager in me says: “It means a risk-weighted approach may lead to early results in the next two quarters.” Naturally, no one wants either phrase, they want something clearer: “Do X and you will get Y.” Tempting sometimes, to take the easy path and just tell them what I know they want to hear.

In a recent blog, Dr. Paul Ekman explains why people with power think they can get away with lies and deception; he builds a compelling narrative. However, his take-home message is that our goal never is merely achieving success. Rather, our goal is: “wanting to make a difference, by altruism and empathy”.

Data never lie, but they are easily misused. They are misused when poorly understood and are misused when a false narrative is supported by an artful display of numbers. In human research, there is the principle of informed consent. It doesn’t mean every human subject must be a biochemist; it simply means that an honest effort is made to explain in plain language the goals, risks, and outcomes of an experiment.

So it must be for the institutional researchers and statisticians of our campus. Data come messy, from thousands of students and hundreds of employees. It can be quite a giddy feeling to access musty corridors in our secure networks, piecing together data like so many arcane scrolls. For a mathematician like me, it is a comforting lie to believe numbers alone can be everything. For a people like me, it is also a comforting lie to believe that if the data are too messy, it means I can just do what I want. Maybe you’ve believed that sort of lie, too.

When data come messy, what it means is that there is noise in the signal, not that there is no signal. Suppose you knew you had to fight a battle and the enemy was out there, and you were desperately tuning your radio to hear from your spies where the attack would strike. You wouldn’t tell your warfighters: “I can’t hear a clear signal kids, let’s just sit in camp.” No! You’d listen as closely as you could to that radio, and then you’d give your orders. Better for morale, and better for survival to do something. Better for survival to do something together as a team.

We know students walking onto our campus face tough odds. It is comfortable to believe there is nothing we can do, comfortable because that exonerates us from what we know may well be a sad ending. But good leaders know that doing nothing is never an option. Good leaders know that sometimes one must fight even without a signal, that the noise around us is of itself a clear cry for action.

Good leaders transform the odds into their people’s favor. It takes wanting to make a difference, by altruism and empathy. Of course, it also takes honestly looking at our own data and relentlessly asking questions. For me, it is back to the drawing board, as I look for our next signal. For you? Well, perhaps it takes asking just the right question. What aspect of transforming the odds against our students makes you curious? I’d be glad to look for your signal, too.



Matt Wiley is a tenured, associate professor of mathematics with awards in both mathematics education and honour student engagement. He earned an assortment of degrees in computer science, business, and pure mathematics from the University of California and Texas A&M systems. He is the director of quality enhancement at Victoria College, assisting in the development and implementation of a comprehensive assessment program to enhance institutional performance outcomes. A programmer, a published author, a mathematician, and a transformational leader, Matt has always melded his passion for writing with his joy of logical problem solving and data science. From the boardroom to the classroom, he enjoys finding dynamic ways to partner with interdisciplinary and diverse teams to make complex ideas and projects understandable and solvable.


  1. Matt – I was going to like this, but I honestly have no idea what it says! I’m glad that you are doing the job you are. Thank you!


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