95th percentile explained.

TODO: collapasable, copyable, download, etc

What does 95th percentile mean?The 95th percentile is a statistical measure used to describe a value below which a certain percentage of observations fall. Specifically, the 95th percentile refers to the value below which 95% of the observations or data points in a given distribution or sample fall.

For example, if you have a dataset of test scores for a group of students, and the 95th percentile score is 90, it means that 95% of the students scored below 90, while only 5% scored 90 or higher. Put another way, if you ranked all the scores in order from lowest to highest, the score at the 95th percentile would be the score that is higher than 95% of the other scores in the dataset.

The 95th percentile is commonly used in fields such as finance, where it can be used to measure risk or evaluate investment returns, as well as in healthcare, where it can be used to measure various health parameters such as blood pressure or BMI.To figure out the 95th percentile on a list of numbers, what do you do with duplicate values?When calculating the 95th percentile on a list of numbers that contains duplicate values, you include all the duplicate values in the calculation.

Here are the general steps to calculate the 95th percentile, including duplicate values:

1. Sort the list of numbers in ascending order.
2. Count the total number of values in the list.
3. Calculate 95% of the total number of values, which is the number of values below the 95th percentile.
4. Round up to the nearest whole number.
5. Identify the value in the sorted list that corresponds to the rounded-up number. This value is the 95th percentile.

For example, suppose you have the following list of numbers:

10, 20, 30, 40, 50, 50, 60, 70, 80, 90

To find the 95th percentile:

1. Sort the list: 10, 20, 30, 40, 50, 50, 60, 70, 80, 90
2. Count the total number of values: 10
3. Calculate 95% of 10: 0.95 x 10 = 9.5
4. Round up to the nearest whole number: 10
5. Identify the value in the sorted list that corresponds to the 10th position, which is 90.

Therefore, the 95th percentile in this list is 90, which is the highest value that is below the top 5% of values in the list.How do you calculate percentile using SQL?To calculate the percentile using SQL, you can use the PERCENT_RANK() window function or the PERCENTILE_CONT() or PERCENTILE_DISC() aggregate functions, depending on the specific requirements of your calculation.

Here is an example using the PERCENT_RANK() window function to calculate the 95th percentile of a column named score in a table named students:

SELECT score
FROM (
  SELECT score, PERCENT_RANK() OVER (ORDER BY score) AS pct_rank
  FROM students
) sub
WHERE pct_rank >= 0.95
ORDER BY score
LIMIT 1;

In this example, the PERCENT_RANK() function is used to calculate the percentile rank of each value in the score column, which is a value between 0 and 1. The subquery then filters the results to include only those rows with a percentile rank greater than or equal to 0.95, and the ORDER BY and LIMIT clauses are used to return the value at the 95th percentile.

Alternatively, you can use the PERCENTILE_CONT() or PERCENTILE_DISC() functions to calculate the percentile directly. Here is an example using PERCENTILE_CONT() to calculate the 95th percentile of the same score column:

SELECT PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY score) AS percentile_score
FROM students;

In this example, the PERCENTILE_CONT() function is used to calculate the continuous percentile (i.e., a percentile that can include fractional values) of the score column, with a percentile rank of 0.95. The WITHIN GROUP (ORDER BY score) clause specifies the column to use for ordering the values. The function returns the value at the 95th percentile, which may include a fractional part.Does SQLite implement these functions?Yes, SQLite does support these percentile functions:

• PERCENT_RANK(): Introduced in SQLite 3.27.0, this is a window function that calculates the relative rank of a row within a result set as a value between 0 and 1.
• PERCENTILE_CONT(): Introduced in SQLite 3.33.0, this is an aggregate function that calculates the continuous percentile of a column.
• PERCENTILE_DISC(): Introduced in SQLite 3.33.0, this is an aggregate function that calculates the discrete percentile of a column.

These functions can be used in a similar way to the SQL examples I provided earlier, with appropriate syntax adjustments for SQLite. For example, here is an example SQLite query that uses PERCENT_RANK() to calculate the 95th percentile of a column named score in a table named students:

SELECT score
FROM (
  SELECT score, PERCENT_RANK() OVER (ORDER BY score) AS pct_rank
  FROM students
) sub
WHERE pct_rank >= 0.95
ORDER BY score
LIMIT 1;

And here is an example using PERCENTILE_CONT() to calculate the 95th percentile of the same score column:

SELECT PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY score) AS percentile_score
FROM students;

Note that these functions may not be available in older versions of SQLite. If you're using an older version of SQLite and need these functions, you may need to update to a newer version.please redo the code samples for a Table shopify and instead of score it is ratings_countCertainly! Here are the updated examples using a table named shopify and a column named ratings_count:

Using PERCENT_RANK():

SELECT ratings_count
FROM (
  SELECT ratings_count, PERCENT_RANK() OVER (ORDER BY ratings_count) AS pct_rank
  FROM shopify
) sub
WHERE pct_rank >= 0.95
ORDER BY ratings_count
LIMIT 1;

Using PERCENTILE_CONT():

SELECT PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY ratings_count) AS percentile_rating_count
FROM shopify;

Using PERCENTILE_DISC():

SELECT PERCENTILE_DISC(0.95) WITHIN GROUP (ORDER BY ratings_count) AS percentile_rating_count
FROM shopify;

In each of these examples, the ratings_count column in the shopify table is used to calculate the percentile, with the specific function used depending on the desired behavior (continuous or discrete percentile).

95th percentile explained. created at Tue Jan 20 1970 10:33:28 GMT+0000 (Coordinated Universal Time)