Guidelines

Methodology

I. Main goal

Give the end-users the ability to choose and specify their own preferences, by separating objective and subjective data for tailored results to the specific needs of each user.

II. Why?

Traditional reviews often blend objective data with subjective opinions, making it difficult for consumers to form fully informed decisions. While subjective viewpoints can be useful, they can also heavily influence consumer choices and, in turn, shape market trends. Tool creators often rely on market data, which is, to some extent, driven by these reviews, to decide what to develop, change, or discontinue. This means that the opinions of a few reviewers can disproportionately influence the direction of entire industries.

Although many reviewers offer caveats like "If you are X, you may not like A" or "If you prefer Y, you’ll love B regardless of Z," this still leaves consumers to interpret a large amount of information and make complex mental calculations. Such a process can lead to mistakes in decision-making due to inherent biases or errors in understanding.

To reduce these errors, we propose a new methodology that clearly separates objective data from subjective opinions. By rating and organizing everything in a structured, data-driven way, users can make decisions based on their own preferences rather than being swayed by a reviewer's personal perspective. This system requires converting all relevant characteristics into quantifiable data, which can then be evaluated using appropriate formulas for each specific attribute.

III. What can be compared?

Anything that holds value and requires significant time or mental effort to evaluate can be compared. This includes, but is not limited to, products, services, techniques, methodologies, formulas, and algorithms ... Essentially, anything that falls under the broad category of "ideas." Whether simple or complex, free or paid, tangible or intangible, if it can improve our lives, it is worth comparing.

However, at this stage, we are prioritizing comparisons of essential products and technologies that most people rely on frequently and that involve substantial costs.

IV. Core components

1. Pre-Summary

This section is where the initial calculation happen, and since most of the time dozens of characteristics are faced, to make it more readable, characteristics are grouped into “Aspects”.

And each characteristic row has:

A. Subjective Data

a. Single Relevance

A way to value a characteristic compared to the other characteristics. Using this, users can determine how important is the characteristic to them compared to the other characteristics by how big their values are. The Calculated Characteristic Factor of the item with the biggest value gets a 100, and the Calculated Characteristic Factor of the item with the smallest value gets a 0, and all results are multiplied by the selected relevance listed below:

• Low Relevance = 1
• Medium Relevance = 2
• High Relevance = 3

b. Reversed Single Relevance

Same as Single Relevance but reversed.

A way to value a characteristic compared to the other characteristics. Using this, users can determine how important is the characteristic to them compared to the other characteristics by how small their values are. The Calculated Characteristic Factor of the item with the smallest value gets a 100, and the Calculated Characteristic Factor of the item with the biggest value gets a 0, and all results are multiplied by the selected relevance listed above.

B. Objective Data

Determined by the tester/comparer/reviewer.

If the characteristic doesn’t exist, then it takes 0. Else:

a. Item Characteristic Quantity

Examples:

• Price (Quantity only).
• Device Storage (Quantity and quality).

b. Item Characteristic Quality

Values are based on:

• Mediocre Quality = 1
• Good Quality = 2
• Better Quality = 3

Examples: (Compared to the other items)

• How fast is the storage device?
• How many writes does it last?

C. Calculations

• Characteristic Factor = Quantity * Quality.
• In Single Relevance: Calculated Characteristic Factor = (Data Normalized Characteristic Factor * 100) * Single Relevance.
• In Reversed Single Relevance: Calculated Characteristic Factor = (Reversed Data Normalized Characteristic Factor * 100) * Single Relevance.

Informational characteristics will be given 0 as the value for all the items.

2. Post-Summary

This section is simply called “Summary”, but it does more: Digest + Calibrate. It is where everything in Pre-Summary is summarized, and when needed, weighting is added, which is more described as allowance or adjustment made in order to take account of special circumstances or compensate for distorting factors.

A. Digest

All rows must be in order:

1. Total (Pts) = SUM (Item Aspects Points).
2. Points (/100) = Data Normalization of the latest.
3. Points (/10) = Latest / 10.
4. Points (/5) = Latest / 2.

a. When price exists

1. Price ($) = The actual price.
2. Price (/100) = Data Normalization of the latest.
3. Price (/10) = Latest / 10.
4. Price (/5) = Latest / 2.
5. Overall (/100) = ((Points (/5) + Price (/5)) / 2) * 20.
6. Overall (/10) = Latest / 10.
7. Overall (/5) = Latest / 2.

All of /100, /10 and /5 are used instead of only one of them, for more flexibility so formulas can be used in-between them when needed.

Latest = Calculated Factor (The result of the last cell above the current cell).

B. Calibrate

This is where it is made fair for more balanced comparison end results. The formulas here can be anything, but can also be separate into two main types:

1. Formulas that consists of objective data.
2. Formulas that consists of subjective data (The end-user should be able to change).