diff --git a/docs/source/concepts/configuration.rst b/docs/source/concepts/configuration.rst
deleted file mode 100644
index 0a9002dc..00000000
--- a/docs/source/concepts/configuration.rst
+++ /dev/null
@@ -1,16 +0,0 @@
-.. _configuration_concept:
-
-========================
-The Configuration System
-========================
-
-.. contents::
-   :depth: 2
-   :local:
-   :backlinks: none
-
-Outline
--------
- - Configuration is hierarchical
- - Configuration sources and priorities
- - Specifying defaults.
\ No newline at end of file
diff --git a/docs/source/concepts/datasets.rst b/docs/source/concepts/datasets.rst
deleted file mode 100644
index 64bc6107..00000000
--- a/docs/source/concepts/datasets.rst
+++ /dev/null
@@ -1,33 +0,0 @@
-.. _datasets_concept:
-
-==============
-   Datasets
-==============
-
-.. contents::
-   :depth: 2
-   :local:
-   :backlinks: none
-
-
-
-
-What is a dataset?
-------------------
-
-A dataset in the Pseudopeople framework contains un-noised simulated data 
-representing specific real-life data, eg a census survey or tax document.
-The types of datasets that are compatible with the Pseudopeople framework include:
-
-.. list-table:: **Types of Datasets**
-   :header-rows: 1
-   :widths: 20
-
-   * - Name
-   * - | Decennial census
-   * - | American communities survey
-   * - | Current population survey
-   * - | Women, infrants, and children survey
-   * - | Social security
-   * - | Tax W2 and 1099 forms
-   * - | Tax 1040 form
diff --git a/docs/source/concepts/index.rst b/docs/source/concepts/index.rst
deleted file mode 100644
index f527da18..00000000
--- a/docs/source/concepts/index.rst
+++ /dev/null
@@ -1,13 +0,0 @@
-.. _concepts_main:
-
-========
-Concepts
-========
-Here we cover several core conceptual topics related to working with
-the Pseudopeople framework.
-
-.. toctree::
-   :glob:
-   :maxdepth: 1
-
-   *
diff --git a/docs/source/concepts/noise_functions.rst b/docs/source/concepts/noise_functions.rst
deleted file mode 100644
index a5eda2f4..00000000
--- a/docs/source/concepts/noise_functions.rst
+++ /dev/null
@@ -1,42 +0,0 @@
-.. _noise_functions_concept:
-
-=================
- Noise Functions
-=================
-
-.. contents::
-   :depth: 2
-   :local:
-   :backlinks: none
-
-
-
-
-What is a noise function?
--------------------------
-
-A noise function is ultimately where the configuration (add link) provided is
-applied to the raw data which is then noised or altered and returned to the user
-in a state where real world data error have been added to each dataset (add link).
-Noise functions will be applied to datasets by column or by row.  There are
-several noise functions that are applied to the raw data which include:
-
-.. list-table:: **Noise Functions**
-   :header-rows: 1
-   :widths: 20
-
-   * - Name
-   * - | Omission
-   * - | Duplications
-   * - | Missing data
-   * - | Incorrect selection
-   * - | Copy from within household
-   * - | Month and day swaps (applies to dates only)
-   * - | Zip Code Miswriting (applies to Zip Code only)
-   * - | Age Miswriting (applies to age only)
-   * - | Numeric miswriting
-   * - | Nicknames
-   * - | Fake names
-   * - | Phonetic errors
-   * - | OCR (optical character recognition)
-   * - | Typographic
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 5beaf57e..98181ad8 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -26,7 +26,7 @@ Introduction
 The University of Washington IHME Simulation Science Team is excited to introduce pseudopeople, the Python package that simplifies Entity Resolution (ER) research and development.
 This package generates large-scale, simulated population data according to specifications by the user, to replicate a range of complexities of real applications of probabilistic record linkage software.
 With sensitive data often required for ER, accessing and testing new methods and software has been a challenge - until now.
-Our innovative approach creates realistic, simulated data including name, address, and date of birth, without compromising privacy. 
+Our innovative approach creates realistic, simulated data including name, address, and date of birth, without compromising privacy.
 
 Our work builds on the success of previous data synthesis projects, such as
 `FEBRL <http://users.cecs.anu.edu.au/~Peter.Christen/Febrl/febrl-0.3/febrldoc-0.3/manual.html>`_,
@@ -35,7 +35,7 @@ and `SOG <https://web.archive.org/web/20170830050229/http:/mitiq.mit.edu/ICIQ/Do
 while leveraging the power of our simulation platform `Vivarium <https://vivarium.readthedocs.io/en/latest/>`_ to incorporate real, publicly-accessible data about the US population.
 This allows us to model realistic household and family structures at scale, with relevant geographies.
 We have created a simulation of the US population, including names and addresses, with defined types of data collection (e.g., simulating decennial censuses, surveys, taxes, and other administrative data).
-By creating realistic, but simulated, data which includes these attributes, we can make ER research and development easier for ourselves and others. 
+By creating realistic, but simulated, data which includes these attributes, we can make ER research and development easier for ourselves and others.
 
 Quickstart
 ----------
@@ -120,6 +120,5 @@ Now, see how many your record linkage method can find -- without access to the t
    noise/index
    configuration/index
    tutorials/index
-   concepts/index
    api_reference/index
    glossary