fix grammar in 2021 posts

1 files changed, 98 insertions, 101 deletions

diff --git a/content/blog/2021-08-25-audit-sampling.org b/content/blog/2021-08-25-audit-sampling.org
index 1f6c244..a2cc1d4 100644
--- a/content/blog/2021-08-25-audit-sampling.org
+++ b/content/blog/2021-08-25-audit-sampling.org
@@ -6,44 +6,40 @@
 * Introduction
 
 For anyone who is familiar with internal auditing, external auditing, or
-consulting, you will understand how tedious audit testing can become
-when you are required to test large swaths of data. When we cannot
-establish an automated means of testing an entire population, we
-generate samples to represent the population of data. This helps ensure
-we can have a small enough data pool to test and that our results still
-represent the population.
-
-However, sampling data within the world of audit still seems to confuse
-quite a lot of people. While some audit-focused tools have introduced
-sampling functionality (e.g. Wdesk), many audit departments and firms
-cannot use software like this due to certain constraints, such as the
-team's budget or knowledge. Here is where this article comes in: we're
-going to use [[https://www.python.org][Python]], a free and open-source
-programming language, to generate random samples from a dataset in order
-to suffice numerous audit situations.
+consulting, you will understand how tedious audit testing can become when you
+are required to test large swaths of data. When we cannot establish an automated
+means of testing an entire population, we generate samples to represent the
+population of data. This helps ensure we can have a small enough data pool to
+test and that our results still represent the population.
+
+However, sampling data within the world of audit still seems to confuse quite a
+lot of people. While some audit-focused tools have introduced sampling
+functionality (e.g. Wdesk), many audit departments and firms cannot use software
+like this due to certain constraints, such as the team's budget or knowledge.
+Here is where this article comes in: we're going to use [[https://www.python.org][Python]], a free and
+open-source programming language, to generate random samples from a dataset in
+order to suffice numerous audit situations.
 
 * Audit Requirements for Sampling
 
-Before we get into the details of how to sample with Python, I want to
-make sure I discuss the different requirements that auditors may have of
-samples used within their projects.
+Before we get into the details of how to sample with Python, I want to make sure
+I discuss the different requirements that auditors may have of samples used
+within their projects.
 
 ** Randomness
 
-First, let's discuss randomness. When testing out new technology to help
-assist with audit sampling, you need to understand exactly how your
-samples are being generated. For example, if the underlying function is
-just picking every 57th element from a list, that's not truly random;
-it's a systematic form of sampling. Luckily, since Python is
-open-source, we have access to its codebase. Through this blog post, I
-will be using the [[https://pandas.pydata.org][pandas]] module in order
-to generate the random samples. More specifically, I will be using the
-[[https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sample.html][pandas.DataFrame.sample]]
-function provided by Pandas.
-
-Now that you know what you're using, you can always check out the code
-behind =pandas.DataFrame.sample=. This function does a lot of work, but
-we really only care about the following snippets of code:
+First, let's discuss randomness. When testing out new technology to help assist
+with audit sampling, you need to understand exactly how your samples are being
+generated. For example, if the underlying function is just picking every 57th
+element from a list, that's not truly random; it's a systematic form of
+sampling. Luckily, since Python is open-source, we have access to its codebase.
+Through this blog post, I will be using the [[https://pandas.pydata.org][pandas]] module in order to generate
+the random samples. More specifically, I will be using the
+[[https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sample.html][pandas.DataFrame.sample]] function provided by Pandas.
+
+Now that you know what you're using, you can always check out the code behind
+=pandas.DataFrame.sample=. This function does a lot of work, but we really only
+care about the following snippets of code:
 
 #+begin_src python
 # Process random_state argument
@@ -59,61 +55,62 @@ result.index = ibase.default_index(len(result))
 return result
 #+end_src
 
-The block of code above shows you that if you assign a =random_state=
-argument when you run the function, that will be used as a seed number
-in the random generation and will allow you to reproduce a sample, given
-that nothing else changes. This is critical to the posterity of audit
-work. After all, how can you say your audit process is adequately
-documented if the next person can't run the code and get the same
-sample? The final piece here on randomness is to look at the
-[[https://docs.python.org/3/library/random.html#random.choice][choice]]
-function used above. This is the crux of the generation and can also be
-examined for more detailed analysis on its reliability. As far as
-auditing goes, we will trust that these functions are mathematically
-random.
+The block of code above shows you that if you assign a =random_state= argument
+when you run the function, that will be used as a seed number in the random
+generation and will allow you to reproduce a sample, given that nothing else
+changes. This is critical to the posterity of audit work. After all, how can you
+say your audit process is adequately documented if the next person can't run the
+code and get the same sample? The final piece here on randomness is to look at
+the [[https://docs.python.org/3/library/random.html#random.choice][choice]] function used above. This is the crux of the generation and can also
+be examined for more detailed analysis on its reliability. As far as auditing
+goes, we will trust that these functions are mathematically random.
 
 ** Sample Sizes
 
-As mentioned in the intro, sampling is only an effective method of
-auditing when it truly represents the entire population. While some
-audit departments or firms may consider certain judgmental sample sizes
-to be adequate, you may need to rely on statistically-significant
-confidence levels of sample testing at certain points. I will
-demonstrate both here. For statistically-significant confidence levels,
-most people will assume a 90% - 99% confidence level. In order to
-actually calculate the correct sample size, it is best to use
-statistical tools due to the tedious math work required. For example,
-for a population of 1000, and a 90% confidence level that no more than
-5% of the items are nonconforming, you would sample 45 items.
-
-However, in my personal experience, many audit departments and firms do
-not use statistical sampling. Most people use a predetermined, often
-proprietary, table that will instruct auditors which sample sizes to
-choose. This allows for uniform testing and reduces overall workload.
-See the table below for a common implementation of sample sizes:
-
-Control Frequency Sample Size - High Risk Sample Size - Low Risk
-
---------------
-
-More Than Daily 40 25 Daily 40 25 Weekly 12 5 Monthly 5 3 Quarterly 2 2
-Semi-Annually 1 1 Annually 1 1 Ad-hoc 1 1
+As mentioned in the intro, sampling is only an effective method of auditing when
+it truly represents the entire population. While some audit departments or firms
+pmay consider certain judgmental sample sizes to be adequate, you may need to
+rely on statistically-significant confidence levels of sample testing at certain
+
+points. I will demonstrate both here. For statistically-significant confidence
+levels, most people will assume a 90% - 99% confidence level. In order to
+actually calculate the correct sample size, it is best to use statistical tools
+due to the tedious math work required. For example, for a population of 1000,
+and a 90% confidence level that no more than 5% of the items are nonconforming,
+you would sample 45 items.
+
+However, in my personal experience, many audit departments and firms do not use
+statistical sampling. Most people use a predetermined, often proprietary, table
+that will instruct auditors which sample sizes to choose. This allows for
+uniform testing and reduces overall workload. See the table below for a common
+implementation of sample sizes:
+
+| Control Frequency | Sample Size (High Risk) | Sample Size (Low Risk) |
+|-------------------+-------------------------+------------------------|
+| More Than Daily   |                      40 |                     25 |
+| Daily             |                      40 |                     25 |
+| Weekly            |                      12 |                      5 |
+| Monthly           |                       5 |                      3 |
+| Quarterly         |                       2 |                      2 |
+| Semi-Annually     |                       1 |                      1 |
+| Annually          |                       1 |                      1 |
+| Ad-hoc            |                       1 |                      1 |
 
 *** Sampling with Python & Pandas
 
-In this section, I am going to cover a few basic audit situations that
-require sampling. While some situations may require more effort, the
-syntax, organization, and intellect used remain largely the same. If
-you've never used Python before, note that lines starting with a '=#='
-symbol are called comments, and they will be skipped by Python. I highly
-recommend taking a quick tutorial online to understand the basics of
-Python if any of the code below is confusing to you.
+In this section, I am going to cover a few basic audit situations that require
+sampling. While some situations may require more effort, the syntax,
+organization, and intellect used remain largely the same. If you've never used
+Python before, note that lines starting with a '=#=' symbol are called comments,
+and they will be skipped by Python. I highly recommend taking a quick tutorial
+online to understand the basics of Python if any of the code below is confusing
+to you.
 
 ** Simple Random Sample
 
-First, let's look at a simple, random sample. The code block below will
-import the =pandas= module, load a data file, sample the data, and
-export the sample to a file.
+First, let's look at a simple, random sample. The code block below will import
+the =pandas= module, load a data file, sample the data, and export the sample to
+a file.
 
 #+begin_src python
 # Import the Pandas module
@@ -137,8 +134,8 @@ sample.to_excel(file_output)
 
 ** Simple Random Sample: Using Multiple Input Files
 
-Now that we've created a simple sample, let's create a sample from
-multiple files.
+Now that we've created a simple sample, let's create a sample from multiple
+files.
 
 #+begin_src python
 # Import the Pandas module
@@ -171,9 +168,9 @@ sample.to_excel(file_output)
 
 ** Stratified Random Sample
 
-Well, what if you need to sample distinct parts of a single file? For
-example, let's write some code to separate our data by "Region" and
-sample those regions independently.
+Well, what if you need to sample distinct parts of a single file? For example,
+let's write some code to separate our data by "Region" and sample those regions
+independently.
 
 #+begin_src python
 # Import the Pandas module
@@ -205,10 +202,10 @@ sample.to_excel(file_output)
 
 ** Stratified Systematic Sample
 
-This next example is quite useful if you need audit coverage over a
-certain time period. This code will generate samples for each month in
-the data and combine them all together at the end. Obviously, this code
-can be modified to stratify by something other than months, if needed.
+This next example is quite useful if you need audit coverage over a certain time
+period. This code will generate samples for each month in the data and combine
+them all together at the end. Obviously, this code can be modified to stratify
+by something other than months, if needed.
 
 #+begin_src python
 # Import the Pandas module
@@ -254,17 +251,17 @@ sample.to_excel(file_output)
 
 *** Documenting the Results
 
-Once you've generated a proper sample, there are a few things left to do
-in order to properly ensure your process is reproducible.
-
-1. Document the sample. Make sure the resulting file is readable and
-   includes the documentation listed in the next bullet.
-2. Include documentation around the data source, extraction techniques,
-   any modifications made to the data, and be sure to include a copy of
-   the script itself.
-3. Whenever possible, perform a completeness and accuracy test to ensure
-   your sample is coming from a complete and accurate population. To
-   ensure completeness, compare the record count from the data source to
-   the record count loaded into Python. To ensure accuracy, test a small
-   sample against the source data (e.g., test 5 sales against the
-   database to see if the details are accurate).
+Once you've generated a proper sample, there are a few things left to do in
+order to properly ensure your process is reproducible.
+
+1. Document the sample. Make sure the resulting file is readable and includes
+   the documentation listed in the next bullet.
+2. Include documentation around the data source, extraction techniques, any
+   modifications made to the data, and be sure to include a copy of the script
+   itself.
+3. Whenever possible, perform a completeness and accuracy test to ensure your
+   sample is coming from a complete and accurate population. To ensure
+   completeness, compare the record count from the data source to the record
+   count loaded into Python. To ensure accuracy, test a small sample against the
+   source data (e.g., test 5 sales against the database to see if the details
+   are accurate).