What to do when there is no data

Earlier, you learned how you can still do an analysis using proxy data if you have no data. You might have some questions about proxy data, so this reading will give you a few more examples of the types of datasets that can serve as alternate data sources.

Proxy data examples

Sometimes the data to support a business objective isn’t readily available. This is when proxy data is useful. Take a look at the following scenarios and where proxy data comes in for each example:

Business scenario	How proxy data can be used
A new car model was just launched a few days ago and the auto dealership can't wait until the end of the month for sales data to come in. They want sales projections now.	The analyst proxies the number of clicks to the car specifications on the dealership's website as an estimate of potential sales at the dealership.
A brand new plant-based meat product was only recently stocked in grocery stores and the supplier needs to estimate the demand over the next four years.	The analyst proxies the sales data for a turkey substitute made out of tofy that has been on the market for several years.
The Chamber of Commerce wants to know how a tourism campaign is going to impact travel to their city, but the results from the campaign aren't publicity available yet.	The analyst proxies the historical data for airline bookings to the city one to three months after a similar campaign was run six months earlier.

Open (public) datasets

If you are part of a large organization, you might have access to lots of sources of data. But if you are looking for something specific or a little outside your line of business, you can also make use of open or public datasets. (You can refer to this Towards Data Science article for a brief explanation of the difference between open and public data.)

Here's an example. A nasal version of a vaccine was recently made available. A clinic wants to know what to expect for contraindications, but just started collecting first-party data from its patients. A contraindication is a condition that may cause a patient not to take a vaccine due to the harm it would cause them if taken. To estimate the number of possible contraindications, a data analyst proxies an open dataset from a trial of the injection version of the vaccine. The analyst selects a subset of the data with patient profiles most closely matching the makeup of the patients at the clinic.

There are plenty of ways to share and collaborate on data within a community. Kaggle (kaggle.com) which we previously introduced, has datasets in a variety of formats including the most basic type, Comma Separated Values (CSV) files.

CSV, JSON, SQLite, and BigQuery datasets

• CSV: Check out this Credit card customers dataset, which has information from 10,000 customers including age, salary, marital status, credit card limit, credit card category, etc. (CC0: Public Domain, Sakshi Goyal).
• JSON: Check out this JSON dataset for trending YouTube videos (CC0: Public Domain, Mitchell J).
• SQLite: Check out this SQLite dataset for 24 years worth of U.S. wildfire data (CC0: Public Domain, Rachael Tatman).
• BigQuery: Check out this Google Analytics 360 sample dataset from the Google Merchandise Store (CC0 Public Domain, Google BigQuery).

Refer to the Kaggle documentation for datasets for more information and search for and explore datasets on your own at kaggle.com/datasets.

As with all other kinds of datasets, be on the lookout for duplicate data and ‘Null’ in open datasets. Null most often means that a data field was unassigned (left empty), but sometimes Null can be interpreted as the value, 0. It is important to understand how Null was used before you start analyzing a dataset with Null data.

Sample size calculator

In this reading, you will learn the basics of sample size calculators, how to use them, and how to understand the results. A sample size calculator tells you how many people you need to interview (or things you need to test) to get results that represent the target population. Let’s review some terms you will come across when using a sample size calculator:

• Confidence level: The probability that your sample size accurately reflects the greater population.
• Margin of error: The maximum amount that the sample results are expected to differ from those of the actual population.
• Population: This is the total number you hope to pull your sample from.
• Sample: A part of a population that is representative of the population.
• Estimated response rate: If you are running a survey of individuals, this is the percentage of people you expect will complete your survey out of those who received the survey.

How to use a sample size calculator

In order to use a sample size calculator, you need to have the population size, confidence level, and the accpetable margin of error already decided so you can input them into the tool. If this information is ready to go, check out these sample size calculators below:

• Sample size calculator by surveymonkey.com

• Sample size calculator by raosoft.com

What to do with the results

After you have plugged your information into one of these calculators, it will give you a recommended sample size. Keep in mind, the calculated sample size is the minimum number to achieve what you input for confidence level and margin of error. If you are working with a survey, you will also need to think about the estimated response rate to figure out how many surveys you will need to send out. For example, if you need a sample size of 100 individuals and your estimated response rate is 10%, you will need to send your survey to 1,000 individuals to get the 100 responses you need for your analysis.

Now that you have the basics, try some calculations using the sample size calculators and refer back to this reading if you need a refresher on the definitions.

Test your knowledge on testing your data

Question 1

A research team runs an experiment to determine if a new security system is more effective than the previous version. What type of results are required for the experiment to be statistically significant?

A. Results that are real and not caused by random chance

B. Results that are hypothetical and in need of more testing

C. Results that are unlikely to occur again

D. Results that are inaccurate and should be ignored

The correct answer is A. Results that are real and not caused by random chance. Explain: In order for an experiment to be statistically significant, the results should be real and not caused by random chance.

Question 2

In order to have a high confidence level in a customer survey, what should the sample size accurately reflect?

A. The trends from other customer surveys

B. The most valuable members of the population

C. The entire population

D. The predictions of stakeholders

The correct answer is C. The entire population. Explain: In order to have a high confidence level in a customer survey, the sample size should accurately reflect the entire population.

Question 3

A data analyst determines an appropriate sample size for a survey. They can check their work by making sure the confidence level percentage plus the margin of error percentage add up to 100%. True or False?

A. True

B. False

Explain: The confidence level percentage and margin of error percentage do not have to add up to 100%. They are independent of each other.