diff --git a/docs/api.rst b/docs/api.rst
index 00ff683da..774deb0d7 100644
--- a/docs/api.rst
+++ b/docs/api.rst
@@ -337,6 +337,7 @@ For more information about fetching data from the internet, see :ref:`fetching t
    workflows.cbma.CBMAWorkflow
    workflows.cbma.PairwiseCBMAWorkflow
    workflows.ibma.IBMAWorkflow
+   workflows.misc.conjunction_analysis
 
 :mod:`nimare.reports`: NiMARE report
 --------------------------------------------------
diff --git a/examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py b/examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py
index 9591c8204..9bbc9bf2e 100644
--- a/examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py
+++ b/examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py
@@ -178,13 +178,9 @@
 # can be computed by (a) identifying voxels that were statistically significant
 # in *both* individual group maps and (b) selecting, for each of these voxels,
 # the smaller of the two group-specific *z* values :footcite:t:`nichols2005valid`.
-# Since this is simple arithmetic on images, conjunction is not implemented as
-# a separate method in :code:`NiMARE` but can easily be achieved with
-# :func:`nilearn.image.math_img`.
-from nilearn.image import math_img
+from nimare.workflows.misc import conjunction_analysis
 
-formula = "np.where(img1 * img2 > 0, np.minimum(img1, img2), 0)"
-img_conj = math_img(formula, img1=knowledge_img, img2=related_img)
+img_conj = conjunction_analysis([knowledge_img, related_img])
 
 plot_stat_map(
     img_conj,
diff --git a/nimare/tests/test_workflows.py b/nimare/tests/test_workflows.py
index 4d00afa22..621545c71 100644
--- a/nimare/tests/test_workflows.py
+++ b/nimare/tests/test_workflows.py
@@ -1,6 +1,8 @@
 """Test nimare.workflows."""
 import os.path as op
 
+import nibabel as nib
+import numpy as np
 import pytest
 
 import nimare
@@ -10,7 +12,12 @@
 from nimare.meta.cbma import ALE, ALESubtraction, MKDAChi2
 from nimare.meta.ibma import Fishers, PermutedOLS, Stouffers
 from nimare.tests.utils import get_test_data_path
-from nimare.workflows import CBMAWorkflow, IBMAWorkflow, PairwiseCBMAWorkflow
+from nimare.workflows import (
+    CBMAWorkflow,
+    IBMAWorkflow,
+    PairwiseCBMAWorkflow,
+    conjunction_analysis,
+)
 
 
 def test_ale_workflow_function_smoke(tmp_path_factory):
@@ -246,3 +253,48 @@ def test_ibma_workflow_smoke(
         filename = f"{tabletype}.tsv"
         outpath = op.join(tmpdir, filename)
         assert op.isfile(outpath)
+
+
+def test_conjunction_analysis_smoke(tmp_path_factory):
+    """Run smoke test for conjunction analysis workflow."""
+    # Create two 3D arrays with random values
+    arr1 = np.random.rand(10, 10, 10)
+    arr2 = np.random.rand(10, 10, 10)
+
+    # Create two Nifti1Image objects from the arrays
+    img1 = nib.Nifti1Image(arr1, np.eye(4))
+    img2 = nib.Nifti1Image(arr2, np.eye(4))
+
+    # Perform conjunction analysis on the two images
+    conj_img = conjunction_analysis([img1, img2])
+
+    # Check that the output is a Nifti1Image object
+    assert isinstance(conj_img, nib.Nifti1Image)
+
+    # Check that the output has the same shape as the input images
+    assert conj_img.shape == img1.shape
+
+    # Check that the output has the correct values
+    expected_output = np.minimum.reduce([arr1, arr2])
+    np.testing.assert_array_equal(conj_img.get_fdata(), expected_output)
+
+    # Test passing in a list of strings
+    tmpdir = tmp_path_factory.mktemp("test_conjunction_analysis_smoke")
+    img1_fn = op.join(tmpdir, "image1.nii.gz")
+    img2_fn = op.join(tmpdir, "image2.nii.gz")
+    img1.to_filename(img1_fn)
+    img2.to_filename(img2_fn)
+
+    # Perform conjunction analysis on the two images from nifti files
+    conj_img_fromstr = conjunction_analysis([img1_fn, img2_fn])
+
+    # Check that the output has the correct values
+    np.testing.assert_array_equal(conj_img.get_fdata(), conj_img_fromstr.get_fdata())
+
+    # Raise error if only one image is provided
+    with pytest.raises(ValueError):
+        conjunction_analysis([img1])
+
+    # Raise error if invalid image type is provided
+    with pytest.raises(ValueError):
+        conjunction_analysis([1, 2])
diff --git a/nimare/workflows/__init__.py b/nimare/workflows/__init__.py
index af045ad25..e8c12b95e 100644
--- a/nimare/workflows/__init__.py
+++ b/nimare/workflows/__init__.py
@@ -4,6 +4,7 @@
 from .cbma import CBMAWorkflow, PairwiseCBMAWorkflow
 from .ibma import IBMAWorkflow
 from .macm import macm_workflow
+from .misc import conjunction_analysis
 
 __all__ = [
     "ale_sleuth_workflow",
@@ -11,4 +12,5 @@
     "PairwiseCBMAWorkflow",
     "IBMAWorkflow",
     "macm_workflow",
+    "conjunction_analysis",
 ]
diff --git a/nimare/workflows/ale.py b/nimare/workflows/ale.py
index 3639e485b..14743eb63 100644
--- a/nimare/workflows/ale.py
+++ b/nimare/workflows/ale.py
@@ -26,7 +26,7 @@ def ale_sleuth_workflow(
 ):
     """Perform ALE meta-analysis from Sleuth text file."""
     LGR.warning(
-        "The ale_sleuth_workflow function is deprecated and will be removed in release 0.1.3. "
+        "The ale_sleuth_workflow function is deprecated and will be removed after release 0.2.0. "
         "Use CBMAWorkflow or PairwiseCBMAWorkflow instead."
     )
 
diff --git a/nimare/workflows/misc.py b/nimare/workflows/misc.py
new file mode 100644
index 000000000..607fda27b
--- /dev/null
+++ b/nimare/workflows/misc.py
@@ -0,0 +1,64 @@
+"""Miscellaneous Workflows."""
+import logging
+
+import nibabel as nib
+from nilearn._utils import check_niimg_3d
+from nilearn.image import math_img
+
+LGR = logging.getLogger(__name__)
+
+
+def conjunction_analysis(imgs):
+    """Perform a conjunction analysis.
+
+    .. versionadded:: 0.2.0
+
+    This method is described in :footcite:t:`nichols2005valid`.
+
+    Parameters
+    ----------
+    imgs : :obj:`list` of 3D :obj:`~nibabel.nifti1.Nifti1Image`, or :obj:`list` of :obj:`str`
+        List of images upon which to perform the conjuction analysis.
+        If a list of strings is provided, it is assumed to be paths to NIfTI images.
+
+    Returns
+    -------
+    :obj:`~nibabel.nifti1.Nifti1Image`
+        Conjunction image.
+
+    References
+    ----------
+    .. footbibliography::
+    """
+    if len(imgs) < 2:
+        raise ValueError("Conjunction analysis requires more than one image.")
+
+    imgs_dict = {}
+    mult_formula, min_formula = "", ""
+    for img_i, img_obj in enumerate(imgs):
+        if isinstance(img_obj, str):
+            img = nib.load(img_obj)
+        elif isinstance(img_obj, nib.Nifti1Image):
+            img = img_obj
+        else:
+            raise ValueError(
+                f"Invalid image type provided: {type(img_obj)}. Must be a path to a NIfTI image "
+                "or a NIfTI image object."
+            )
+
+        img = check_niimg_3d(img)
+
+        img_label = f"img{img_i}"
+        imgs_dict[img_label] = img
+        mult_formula += img_label
+        min_formula += img_label
+
+        if img_i != len(imgs) - 1:
+            mult_formula += " * "
+            min_formula += ", "
+
+    formula = f"np.where({mult_formula} > 0, np.minimum.reduce([{min_formula}]), 0)"
+    LGR.info("Performing conjunction analysis...")
+    LGR.info(f"Formula: {formula}")
+
+    return math_img(formula, **imgs_dict)
diff --git a/setup.cfg b/setup.cfg
index b5c48039c..bdfd80ec5 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -46,7 +46,7 @@ install_requires =
     nibabel>=3.2.0  # I/O of niftis
     nilearn>=0.10.1
     numba>=0.57.0 # used by sparse
-    numpy>=1.21
+    numpy>=1.22 # numba needs NumPy 1.22 or greater
     pandas>=2.0.0
     patsy  # for cbmr
     plotly  # nimare.reports
@@ -92,7 +92,7 @@ minimum =
     matplotlib==3.5.2
     nibabel==3.2.0
     nilearn==0.10.1
-    numpy==1.21
+    numpy==1.22
     pandas==2.0.0
     pymare==0.0.4rc2
     scikit-learn==1.0.0