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edited Oct 18 at 16:28

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Well, anyone who looks at your data (thanks for providing btw) and has at least 1 eye, should know immediately that something "suspicious" happened after 2022.
But now you have to prove this to your boss (or rather the boss of the boss of your boss), with some appropriate statistical mumbo-jumbo, which said boss of the boss of the boss will not understand, but which he/she will find convincing.
OK, so you only have shipments per month. You can pehaps reduce this to shipments per day? (taking into account the days where shipping does not occur -week-ends, holidays, etc.??-). E.g before 2022, you had 14 shipments out of ~4000 days. After 2022, you have ~37 shipments out of ~700 days (I am eyeballing it but I should be in the ballpark, and as you will see later, it does not really matter). YourThese go in a 2x2 contingency table, and your p-value is a big, fat 0 (ok very close to 0, no matter which test you use: Fisher-exact, $\chi^2$, etc.). You can also do it by month, and get the exact same answer ($p\approx 0$). You can also look at the proportions (shipments per some unit of time, which will be fractional), and use a z-test of 2 proportions, and still get a significant result.
xkcd has this great cartoon which states that one should "always try to get data that is good enough that you don't need to do statistics on it". Well, pat yourself on the back, you just did it.
You can use a similar approach (comparing frequency of shipments of goods, before and after), for all your categories of goods.

Well, anyone who looks at your data (thanks for providing btw) and has at least 1 eye, should know immediately that something "suspicious" happened after 2022.
But now you have to prove this to your boss (or rather the boss of the boss of your boss), with some appropriate statistical mumbo-jumbo, which said boss of the boss of the boss will not understand, but which he/she will find convincing.
OK, so you only have shipments per month. You can pehaps reduce this to shipments per day? (taking into account the days where shipping does not occur -week-ends, holidays, etc.??-). E.g before 2022, you had 14 shipments out of ~4000 days. After 2022, you have ~37 shipments out of ~700 days (I am eyeballing it but I should be in the ballpark, and as you will see later, it does not really matter). Your p-value is a big, fat 0 (ok very close to 0, no matter which test you use: Fisher-exact, $\chi^2$, etc.). You can also do it by month, and get the exact same answer ($p\approx 0$). You can also look at the proportions (shipments per some unit of time, which will be fractional), and use a z-test, and still get a significant result.
xkcd has this great cartoon which states that one should "always try to get data that is good enough that you don't need to do statistics on it". Well, pat yourself on the back, you just did it.

Well, anyone who looks at your data (thanks for providing btw) and has at least 1 eye, should know immediately that something "suspicious" happened after 2022.
But now you have to prove this to your boss (or rather the boss of the boss of your boss), with some appropriate statistical mumbo-jumbo, which said boss of the boss of the boss will not understand, but which he/she will find convincing.
OK, so you only have shipments per month. You can pehaps reduce this to shipments per day? (taking into account the days where shipping does not occur -week-ends, holidays, etc.??-). E.g before 2022, you had 14 shipments out of ~4000 days. After 2022, you have ~37 shipments out of ~700 days (I am eyeballing it but I should be in the ballpark, and as you will see later, it does not really matter). These go in a 2x2 contingency table, and your p-value is a big, fat 0 (ok very close to 0, no matter which test you use: Fisher-exact, $\chi^2$, etc.). You can also do it by month, and get the exact same answer ($p\approx 0$). You can also look at the proportions (shipments per some unit of time, which will be fractional), and use a z-test of 2 proportions, and still get a significant result.
xkcd has this great cartoon which states that one should "always try to get data that is good enough that you don't need to do statistics on it". Well, pat yourself on the back, you just did it.
You can use a similar approach (comparing frequency of shipments of goods, before and after), for all your categories of goods.

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created Oct 18 at 4:16

jginestet

5.3k
1
6
19

Well, anyone who looks at your data (thanks for providing btw) and has at least 1 eye, should know immediately that something "suspicious" happened after 2022.
But now you have to prove this to your boss (or rather the boss of the boss of your boss), with some appropriate statistical mumbo-jumbo, which said boss of the boss of the boss will not understand, but which he/she will find convincing.
OK, so you only have shipments per month. You can pehaps reduce this to shipments per day? (taking into account the days where shipping does not occur -week-ends, holidays, etc.??-). E.g before 2022, you had 14 shipments out of ~4000 days. After 2022, you have ~37 shipments out of ~700 days (I am eyeballing it but I should be in the ballpark, and as you will see later, it does not really matter). Your p-value is a big, fat 0 (ok very close to 0, no matter which test you use: Fisher-exact, $\chi^2$, etc.). You can also do it by month, and get the exact same answer ($p\approx 0$). You can also look at the proportions (shipments per some unit of time, which will be fractional), and use a z-test, and still get a significant result.
xkcd has this great cartoon which states that one should "always try to get data that is good enough that you don't need to do statistics on it". Well, pat yourself on the back, you just did it.