Scroll through social media for five minutes and you will probably see a science claim: a drink that "detoxes" your body, a phone setting that "doubles" battery life, a post saying a cold winter proves the climate is not warming. Some claims are true, many are half true, and some are made up to sell something or get clicks. Scientific literacy means being able to find good information, judge whether a claim is backed by evidence, and use science to make decisions about your health, your community and the environment. These skills matter long after this course ends.
A research process
- Write a focused research question. "How does road salt affect freshwater streams in Ontario?" is easier to research than "Is salt bad?"
- Search for sources. Use specific key words, and look at more than one kind of source: government and university sites, science news, encyclopedias, books and databases.
- Judge each source's reliability using the checklist below, and keep only trustworthy ones.
- Combine what the reliable sources say into an answer in your own words, noting where sources agree or disagree.
- Share your findings with a list of the sources you used (citations), so others can check your work.
| Check | Ask yourself | Warning sign |
|---|---|---|
| Author | Who wrote it? Do they have training or experience in this field? | No author named, or an expert in a different field |
| Date | When was it published or updated? Is it recent enough for this topic? | Old information on a fast-changing topic, such as technology or climate data |
| Evidence | Does it give data, explain how it was collected, and link to studies? | Only personal stories or "studies show" with no study named |
| Purpose and bias | Is it trying to inform, or to sell, persuade or get clicks? | Selling a product, emotional language, a headline the article does not back up |
| Peer review and agreement | Was the research checked by other experts? Do other reliable sources agree? | One study that contradicts many others, reported as if it settles the question |
Claim, evidence, reasoning
A scientific argument has three parts. The claim is a statement that answers a question ("Salt lowers the temperature at which water freezes"). The evidence is the data that back it up ("Pure water froze at 0.0 °C; water with 5 g of salt in 100 mL froze at −2.9 °C; with 10 g it froze at −6.0 °C"). The reasoning explains why the evidence supports the claim, using a science idea ("Dissolved salt particles get in the way of water particles joining into ice, so the water must be colder before it can freeze. More salt gets in the way more, so the freezing point drops further"). When you read someone else's claim, look for all three parts. A claim with no evidence, or evidence with no reasoning connecting it, is weak.
Correlation is not causation
Two things are correlated when they change together. In summer, ice cream sales go up and so do drownings. Does ice cream cause drowning? No. A third factor, hot weather, causes both: more people buy ice cream and more people go swimming. Causation means one thing actually makes the other happen, and showing it usually takes a controlled experiment where only one variable is changed. When you see "people who do X are healthier", ask: could something else explain both? Also watch for these kinds of bias, which can lead people to unfair conclusions:
- Funding or sponsor bias: a study paid for by a company that sells the product may be designed or reported in a way that favours it. Check who paid for the research.
- Confirmation bias: everyone tends to notice and remember evidence that agrees with what they already believe, and ignore the rest.
- Cherry-picking: choosing only the data that support a claim, such as quoting one cold week to argue the climate is not warming.
- Small or unrepresentative samples: a survey of 10 friends, or of only one age group, may not reflect everyone.
Science in society: artificial intelligence and new technology
Artificial intelligence (AI) systems are computer programs that learn patterns from huge amounts of data. Scientists now use AI to help forecast weather, spot signs of disease in medical scans and predict the 3-D shapes of proteins. The 2024 Nobel Prize in Physics went to John Hopfield and Geoffrey Hinton of the University of Toronto for discoveries that made modern AI learning possible, and the 2024 Nobel Prize in Chemistry honoured work on computer design and prediction of protein structures. AI also raises real questions. Chatbots can state wrong facts confidently, AI trained on biased data can give unfair results, large data centres use a lot of electricity and water, and some jobs are changing as AI takes over routine tasks while new jobs appear. Science and technology are never used in a vacuum: what gets researched, who pays for it, and who benefits are shaped by the economy, by culture and by society's choices. A scientifically literate person checks AI output against reliable sources, the same way they would check any other source.
| Person | Community or place | Contribution |
|---|---|---|
| Frederick Banting and Charles Best | University of Toronto | With J.J.R. Macleod and James Collip, discovered insulin (1921–22), turning type 1 diabetes from a fatal disease into a manageable one. Banting shared the 1923 Nobel Prize. |
| Elizabeth (Elsie) MacGill | Toronto; Fort William (now Thunder Bay), Ontario | First woman in Canada to earn an electrical engineering degree (1927), and one of the first women anywhere to earn a master's degree in aeronautical engineering (1929). As chief aeronautical engineer, oversaw production of Hawker Hurricane fighter planes in the Second World War. |
| Donna Strickland | University of Waterloo | Shared the 2018 Nobel Prize in Physics for a way to make very short, intense laser pulses, now used in laser eye surgery. |
| Arthur McDonald | Queen's University; Sudbury Neutrino Observatory | Shared the 2015 Nobel Prize in Physics for showing that neutrinos (tiny particles from the Sun) change type, which means they have mass. |
| Elder Albert Marshall | Mi'kmaw Nation, Unama'ki (Cape Breton), Nova Scotia | Introduced Two-Eyed Seeing (Etuaptmumk): learning to see with the strengths of Indigenous knowledges through one eye and Western science through the other, and using both eyes together. |
Indigenous knowledge systems are built from careful observation of the land, water, plants, animals and sky over many generations. For example, Inuit hunters' detailed knowledge of sea ice and First Nations knowledge of fire, plants and animal populations now inform research on climate change and wildlife management. Two-Eyed Seeing asks scientists to respect both ways of knowing and bring them together, rather than treating one as the only kind of knowledge.