diff --git a/movement/analysis/filtering.py b/movement/analysis/filtering.py
new file mode 100644
index 000000000..b4db663e5
--- /dev/null
+++ b/movement/analysis/filtering.py
@@ -0,0 +1,136 @@
+from datetime import datetime
+from typing import Union
+
+import numpy as np
+import xarray as xr
+
+
+def interp_pose(
+    ds: xr.Dataset,
+    method: str = "linear",
+    limit: Union[int, None] = None,
+    max_gap: Union[int, None] = None,
+    inplace: bool = False,
+) -> Union[xr.Dataset, None]:
+    """
+    Fills in NaN values by interpolating over the time dimension.
+
+    Parameters
+    ----------
+    ds : xarray.Dataset
+        Dataset containing pose tracks, confidence scores, and metadata.
+    method : str
+        String indicating which method to use for interpolation.
+        Default is `linear`. See documentation for
+        `xarray.DataSet.interpolate_na` for complete list of options.
+    limit : int | None
+        Maximum number of consecutive NaNs to interpolate over.
+        `None` indicates no limit, and is the default value.
+    max_gap : TODO: Clarify the difference between `limit` & `max_gap`
+        The largest gap of consecutive NaNs that will be
+        interpolated over. The default value is `None`.
+    inplace: bool
+        If true, updates the provided DataSet in place and returns
+        `None`.
+
+    Returns
+    -------
+    ds_thresholded : xr.DataArray
+        The provided dataset (ds), where NaN values have been
+        interpolated over using the parameters provided.
+    """
+    # TODO: This method interpolates over confidence values as well.
+    #  -> Figure out whether this is the desired default behavior.
+    ds_interpolated = ds.interpolate_na(
+        dim="time", method=method, limit=limit, max_gap=max_gap
+    )
+
+    # Logging
+    log_entry = {
+        "operation": "interp_pose",
+        "method": method,
+        "limit": limit,
+        "max_gap": max_gap,
+        "inplace": inplace,
+        "datetime": str(datetime.now()),
+    }
+    ds_interpolated.attrs["log"].append(log_entry)
+
+    if inplace:
+        ds["pose_tracks"] = ds_interpolated["pose_tracks"]
+        ds["confidence"] = ds_interpolated["confidence"]
+        return None
+    else:
+        return ds_interpolated
+
+
+def filter_confidence(
+    ds: xr.Dataset,
+    threshold: float = 0.6,
+    inplace: bool = False,
+    interp: bool = False,
+) -> Union[xr.Dataset, None]:
+    """
+    Drops all datapoints where the associated confidence value
+    falls below a user-defined threshold.
+
+    Parameters
+    ----------
+    ds : xarray.Dataset
+        Dataset containing pose tracks, confidence scores, and metadata.
+    threshold : float
+        The confidence threshold below which datapoints are filtered.
+        A default value of `0.6` is used.
+    inplace : bool
+        If true, updates the provided DataSet in place and returns
+        `None`.
+    interp : bool
+        If true, NaNs are interpolated over using `interp_pose` with
+        default parameters.
+
+    Returns
+    -------
+    ds_thresholded : xarray.Dataset
+        The provided dataset (ds), where datapoints with a confidence
+        value below the user-defined threshold have been converted
+        to NaNs
+    """
+
+    ds_thresholded = ds.where(ds.confidence >= threshold)
+
+    # Diagnostics
+    print("\nDatapoints Filtered:\n")
+    for kp in ds.keypoints.values:
+        n_nans = np.count_nonzero(
+            np.isnan(ds_thresholded.confidence.sel(keypoints=f"{kp}").values)
+        )
+        n_points = ds.time.values.shape[0]
+        prop_nans = round((n_nans / n_points) * 100, 2)
+        print(f"{kp}: {n_nans}/{n_points} ({prop_nans}%)")
+
+    # TODO: Is this enough diagnostics? Should I write logic to allow
+    #  users to optionally plot out confidence distributions + imposed
+    #  threshold?
+
+    # Logging
+    if "log" not in ds_thresholded.attrs.keys():
+        ds_thresholded.attrs["log"] = []
+
+    log_entry = {
+        "operation": "filter_confidence",
+        "threshold": threshold,
+        "inplace": inplace,
+        "datetime": str(datetime.now()),
+    }
+    ds_thresholded.attrs["log"].append(log_entry)
+
+    # Interpolation
+    if interp:
+        interp_pose(ds_thresholded, inplace=True)
+
+    if inplace:
+        ds["pose_tracks"] = ds_thresholded["pose_tracks"]
+        ds["confidence"] = ds_thresholded["confidence"]
+        return None
+    if not inplace:
+        return ds_thresholded