diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 2414687..123fe4b 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -12,16 +12,14 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ["3.10", "3.11"] + python-version: ["3.10", "3.11", "3.12"] steps: - name: Checkout - uses: actions/checkout@v3 + uses: actions/checkout@v4 - name: Set up PDM - uses: pdm-project/setup-pdm@v3 + uses: pdm-project/setup-pdm@v4 with: python-version: ${{ matrix.python-version }} - prerelease: false - enable-pep582: false cache: true - name: Install dependencies run: pdm sync -G :all diff --git a/README.md b/README.md index 5adf3df..3cc934c 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ The Dunning–Kruger effect suggests that people with low competence in a domain ## Try the live demo -Experience the simulation firsthand. Visit the app hosted on Streamlit Community Cloud: [Random number simulation of the Dunning and Kruger experiments](https://dunning-kruger.streamlit.app). +Visit the app hosted on Streamlit Community Cloud: [Random number simulation of the Dunning and Kruger experiments](https://dunning-kruger.streamlit.app). ## Try it locally diff --git a/app.py b/app.py index 0469fa7..b09a18e 100644 --- a/app.py +++ b/app.py @@ -21,7 +21,7 @@ QUARTILES = ("bottom", "2nd", "3rd", "top") -def percentile(x: npt.NDArray[np.float_]) -> npt.NDArray[np.int_]: +def percentile(x: npt.NDArray[np.float64]) -> npt.NDArray[np.int64]: return x.argsort().argsort() * 100 // len(x) def generate_data( @@ -56,8 +56,8 @@ def generate_data( def create_point_chart(data: pl.DataFrame, x: str, y: str) -> alt.Chart: return alt.Chart(data).mark_point().encode( - alt.X(f"{x}:Q").title(x.replace("_", " ")), - alt.Y(f"{y}:Q").title(y.replace("_", " ")), + alt.X(f"{x}:Q").title(x.replace("_", " ")), # type: ignore + alt.Y(f"{y}:Q").title(y.replace("_", " ")), # type: ignore ) @@ -70,21 +70,21 @@ def create_quartile_chart(data: pl.DataFrame, quartile_col: str) -> alt.Chart: ) .group_by(quartile_col) .mean() - .melt(id_vars=quartile_col, value_name="average") + .unpivot(index=quartile_col, value_name="average") .pipe(alt.Chart) .mark_line(point=True) .encode( alt.Color("variable:N") - .sort(("test score", "perceived ability")) + .sort(("test score", "perceived ability")) # type: ignore .legend(orient="bottom-right") .title(None), alt.X(f"{quartile_col}:N") - .sort(QUARTILES) + .sort(QUARTILES) # type: ignore .axis(labelAngle=0) .title(quartile_col.replace("_", " ")), alt.Y("average:Q") .scale(domain=(0, 100)) - .title("average percentile"), + .title("average percentile"), # type: ignore ) ) @@ -152,11 +152,13 @@ def custom_theme() -> dict[str, Any]: """)) st.markdown(textwrap.dedent("""\ - The Dunning–Kruger effect suggests that people with low competence in a domain\ - often overestimate their abilities. But do the foundational experiments truly\ - confirm this effect? This app offers a random number simulation challenging\ - those original findings. For a deep dive into the topic, check out my blog post\ - [Debunking the Dunning–Kruger effect with random number simulation](https://e10v.me/debunking-dunning-kruger-effect/). + The Dunning–Kruger effect is a cognitive bias wherein people with limited\ + competence in a particular domain overestimate their abilities. It turns out\ + that the Dunning and Kruger experiments do not prove that the effect is real.\ + This app illustrates this using a random number simulation. The data generation\ + process doesn't imply that "incompetent" participants overestimate their\ + abilities. See more details in my blog post [Debunking the Dunning–Kruger\ + effect with random number simulation](https://e10v.me/debunking-dunning-kruger-effect/). 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