Cookie Cutter Argument Form: Phenomenon Hypothesis

Lucas Carter

I am back to discuss another cookie cutter argument form. Here is the link to the cost benefit argument structure that I posted about previously: https://7sage.com/discussion/#/discussion/21220

This one is known as Phenomenon Hypothesis. In this argument form, an observation about the world is made, followed by a proposed explanation. This post will discuss some common answer choice types LSAC uses to effect the strength of a hypothesis in explaining a phenomenon or observed occurrence.

1. Affirm/Deny Mechanism

Tells us exactly how the hypothesis would explain the phenomenon.

For example, if I say: there is a correlation between white blood cells and strong immune systems, therefore white blood cells cause strong immune systems.

A mechanism would be explaining a plausible way for white blood cells to improve immune systems. Like: white blood cells contain disease fighting chemicals that kill all bad bacteria. So this information strengthens our hypothesis by providing a plausible mechanism.

To deny the mechanism or weaken, we would show that white blood cells have nothing to do with the immune system.

2. Corroborating Data Set

This is when we bring in a new data set which corroborates or jives with the notion that our hypothesis explains our phenomenon.

For example, if I say: bees left a part of Florida that was experiencing a heat wave, so it probably was the heat which drove them out.

A corroborating data set could show that a heat wave happened recently in Nevada and the bees left as soon as it began. This corroborates our hypothesis and makes it stronger by showing that we introduced the purported cause and got the intended effect, right away. This does not make our hypothesis have to be true, but it does make it more plausible or strengthen it.

3. Competing Data Set

The opposite of a corroborating data set. So, a new set of info that makes our hypothesis a less attractive means of explaining the phenomenon.

To stick with the bee example, we could show that another state experienced a heat wave and the bees stayed put. This would show that we have our purported cause without the effect. This does not kill the argument entirely, but it does weaken or make it slightly less plausible.

4. Consequences

Science operates on eliminating hypotheses. We determine what would be necessary if a hypothesis were true. Such that:

Hypotheses true——> Consequences True

Next, we test those consequences. If they are not true, the hypotheses is not true. If they are true, our hypotheses does not need to be true but it lives to fight another day. We then find more additional consequences that would be true and test those. The hypothesis that survives this consequence testing is deemed best and closest to truth, until proven otherwise.

Example:

There was a UFO sighted over Nevada, close to Area 51, it must be aliens.

A consequence of this hypothesis being true would be that aliens exist, are able to travel, or can build things. If we find out any of these are untrue, the hypothesis is no longer possible.

This form is sort of like a Necessary Assumption for science.

5. Block/Introduce Alternative

This answer choice would either build up or break down a competing hypothesis.

In our Alien example, we could say that the US military was conducting weapons testing during the time the UFO was reported and in close proximity to the sighting.

This being true would explain the observed phenomenon without our hypothesis needing to be true. It also is more plausible than our hypothesis. So, our argument would be weakened.

To block out such an alternative, we would just say that the US military was on holiday the day of the sighting and conducted 0 activity in Nevada. Ruling out an alternative hypothesis, helps make our hypothesis slightly more likely.

6. Temporal Affirmation

If a hypothesis is going to explain a phenomenon, it needs to make sense time wise.

For example:

On Monday, it rained and the highway had 35 car accidents. Normally, there are only 10 accidents per day. I hypothesize the rain created poor driving conditions and thus more accidents.

For this to work, we need the additional accidents to have happened after the rain. To strengthen the hypothesis, we say that the day was average at first and the accidents piled up after the rain

To weaken this, we show that there were already 32 accidents that day, before the rain.

7. Irrelevant

Most Answer choices you see on phenomenon hypotheses questions will have nothing to do with how the hypothesis explains the phenomenon.

Always ask yourself: Does this piece of information have any bearing on how the hypothesis explains the observed phenomenon?

For our Alien example, some irrelevant answer choices might look like:

• Aliens are more intelligent than Lizards.

• Human beings do not have sophisticated enough means to communicate with Aliens

• The UFO was sighted by 3 people with doctorate degrees

• A similar sighting happened in Nebraska, in 1984.

These things are all great, but they do not address whether or not the object was in fact Aliens!

This list is not meant to be exhaustive and I am sure there are many other ways to strengthen or weaken such arguments. Feel free to share any others below

Trusttheprocess

Spitting out super hot fire as usual! LOL

Logician

@"Lucas Carter" good stuff! Very clear and thorough explanations.

youbbyun

Thank you!

Mike_Ross

Nice! Right back at it I see

Lolo1996

Hey! I’ve been waiting for your next post haha. My eyes are always peeled.

More trap answers include:
- What people think, feel, believe, wish, want, their intentions, etc.
- Wrong group - like if the stimulus is discussing pink ducks, and the AC is talking about blue ducks. It can be very subtle.
- The other X, another X, is better than/greater/faster etc.

A little off topic, but what is your approach on 58-4-23? This is also a strengthen question, where a corroborating dataset just doesn’t cut it. I think I was a little traumatized from the question.

Lucas Carter

@Lolo1996 said:
Hey! I’ve been waiting for your next post haha. My eyes are always peeled.

More trap answers include:
- What people think, feel, believe, wish, want, their intentions, etc.
- Wrong group - like if the stimulus is discussing pink ducks, and the AC is talking about blue ducks. It can be very subtle.
- The other X, another X, is better than/greater/faster etc.

A little off topic, but what is your approach on 58-4-23? This is also a strengthen question, where a corroborating dataset just doesn’t cut it. I think I was a little traumatized from the question.

Thanks for your kind words! I posted a detailed explanation in the comments section. That question is pretty tough, but cracks right open if you focus on the argument!

LSATulcer

Wow. Just,wow. This was what I needed. Thank you so much.

leedzzzz

Does anyone have examples of more competing data set answers?