Finding Truth
How Reasoning Can Help Us Determine If We Should Believe Something
An ununified variety of dictionaries, combined with the evolution of language, has resulted in words having similar and very different definitions. However, words matter, and it’s essential to understand their intentions when communicating and having sincere discussions. Therefore, because I want to be honest, I will define what I mean when I say truth.
Truth is objective, which means it is either true or not true. There is no middle ground, and there is no gray area. For example, if I claim I’m legally married, there would be an objective truth. I’m legally married, or I’m not. Another example is the claim that we landed on the moon. That either happened, or it did not. I cannot “sort of” be legally married, and we cannot “kind of” land on the moon.
Now, if I claim I’m legally married, it is possible that I will no longer be legally married at some point in the future. So, the objective truth, in this case, could change depending on when the objective truth is sought. Alternatively, the moon landing in 1969 is an objective truth that cannot change without discovering time travel.
The debate around vaccines and their alleged cause of autism presents another opportunity for an example of objective truth. The argument is not whether vaccines cause autism in all children or no children. The debate is about whether vaccines cause autism in some children or no children. The objective truth is vaccines cause autism in some children, or they do not.
Objective truth is true despite individual beliefs or personal truths. For example, suppose there was a man who robbed a convenience store wearing shorts. Two people claimed they saw the man rob the store. One claimed the robber wore shorts. The other claimed he wore jeans. These are their “personal” truths. Despite both claims, there is an objective truth. The robber was wearing shorts. Now let’s pretend the robber started the robbery wearing shorts but changed into jeans sometime during the process. In this case, the objective truth would be that he changed clothes.
Objective truth excludes individual preferences, emotions, and sensory experiences because these are unique and subjective to each individual. These are subjective truths. Preferences are individual inclinations or choices based on personal likes, dislikes, and values. They are shaped by a person's unique experiences, background, and psychological makeup. Since preferences differ significantly among people, they are not universally consistent and, therefore, not considered objective truths.
Emotions are complex psychological responses to various stimuli, events, or thoughts. A combination of biological, psychological, and environmental factors influences them. Emotions are inherently personal and can vary significantly from one individual to another. Because they are subjective experiences, emotions cannot be universally validated as objective truths.
Sensory experiences are perceptions of the external world through the senses—sight, sound, touch, taste, and smell. Individual sensory sensitivities, cultural influences, and cognitive interpretations influence these experiences. Given the variability of sensory perceptions among individuals, they are not considered objective truths.
Objective truths, in contrast, are facts that can be verified independently and are not contingent on individual perspectives, opinions, biases, interpretations, or preferences. While preferences, emotions, and sensory experiences are valuable aspects of human experience, their subjectivity prevents them from being classified as objective truths.
Someone’s favorite color, food, or design style are all examples that represent preferences. If someone wins the lottery, they are more than likely going to feel happy. Their happiness is an emotional response. Furthermore, if a doctor asked a patient to rate his pain on a scale of 0-10, and the patient replied with a 5, that would be a sensory experience. Finally, if Jim gets “butterflies in his stomach” when he sees Jan, that’s an example of sexual attraction.
Is Truth Desired?
Remember that scene from “A Few Good Men” when Tom Cruise is interrogating Jack Nicholson, and he yells, “I want the truth!”? Remember Nicholson’s reply? He said, “You can’t handle the truth!” These two movie quotes accurately represent the state of many people.
Before looking for truth, one must determine if truth is desired. Some people are happy living in their own world and don’t care if their beliefs are true. As long as their beliefs make them feel good or justify their outlook on life, they are happy to continue believing them. Many people say they desire truth, but if that truth contradicts their current beliefs, they cannot reconcile it.
To find the truth, we must be willing to examine all available evidence to the best of our ability. We must keep a consistent standard of integrity and attempt to set aside our biases, not allowing them to affect our conclusions. This combination is the most reliable way to ensure we believe as many true things as possible.
Believing things that aren’t true can have potentially dire consequences on our decisions and our lives. For example, if one believed vaccines caused autism in children, and this belief prevented them from vaccinating their child, it could cause the child to become sick and possibly die. Conversely, if someone believes vaccines are perfectly safe and they proceed to vaccinate their child, it might inadvertently lead to harmful effects, such as autism.
And finally, we must be willing to be wrong. Our opinions, emotions, and current beliefs have to take a backseat and allow the evidence to drive. They can observe and be part of the ride but cannot control the vehicle. If we allow them to overrule the evidence, we are not allowing the evidence to speak for itself and could more easily believe something false.
Suppose someone held a belief that all Democrats are stupid. If that person allowed their opinion to continue driving their belief, it might cause them to make irrational decisions. If two people were running in an election, with one being a Democrat and one being a Republican, and this person allowed their bias against Democrats to cause them to vote for the Republican, it could result in the vote for a less qualified candidate. This belief exemplifies why evidence must be front and center instead of our emotions.
Finding Truth
When I took a statistics class in college, my professor said, “When we look at data, we’re not attempting to determine absolute certainty. We’re determining a level of confidence, and once we reach that level of confidence, we either reject or fail to reject the null hypothesis.”
I will define each term to demonstrate how my professor's quote helps discover truth and continue my theme of being honest. Let’s begin with confidence. Confidence is a level of certainty that is supported by evidence. It is a spectrum that can and will shift depending on how convinced we are in a claim. For example, if we held a high level of confidence in the claim that man landed on the moon, then we are convinced this claim is likely true. Alternatively, if we weren’t convinced man landed on the moon, we would hold a low confidence level in the claim.
We can determine a confidence level in several ways because of multiple data types and evidence. Some claims are more straightforward, and others have a range. To more easily demonstrate a confidence level, I will use an example with a range of data.
If we were researching the number of Grizzly bears living in Montana and found good evidence to suggest that between 1,800 - 2,000 bears live there, we could be very confident there weren’t 3,000 because this is well outside the range. Now, there could be more than 2,000 or less than 1,800; however, the farther we get from the range, the less confident we become in the number of bears. Are we absolutely certain how many grizzlies live in Montana? No, but we have confidence about the number of bears because of our evidence.
However, our uncertainty of the exact amount doesn’t mean there isn’t an exact number of bears. The number of Grizzlies living within the borders of the state of Montana is an objective truth, even though we may not know that exact number. That objective truth could change because bears don’t have borders. Bears. Borders. Battlestar Galactica. This uncertainty is one reason this data is presented as a range and not an exact number. I imagine another reason is likely because whoever is tracking this data doesn’t have a method of ensuring they’ve accounted for every bear living in Montana.
One thing to remember is that new evidence can always come forward. Suppose we came back a year later and examined the evidence again, and the data now revealed that between 1,500 and 1,700 Grizzly bears live in Montana. It would be fallacious to remain confident in our original data because it changed. If this were the case, and we had new data on the bears, we’d have to adjust our confidence level to match the new, more accurate data. This change of data is why it’s okay for our confidence levels to vary over time if we acquire new or better data.
For this reason, others may poke fun or claim that we don’t hold consistent views. If this happens, it’s because others are conditioned to believe something and stick to that belief, and they do not comprehend the standards of evidence. Anyone who argues that beliefs must stay consistent is ignorant. Politicians often fall on both sides of this fence. They never change their views regardless of new evidence, or they change them and get called “wishy-washy.”
Now, let’s discuss rejecting or failing to reject the null hypothesis. A hypothesis is defined as a supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation, and null is defined as “invalid.” So, a null hypothesis is an invalid proposed explanation, basically a claim. In statistics, we start with a null hypothesis and then gather data (evidence) to see if our null hypothesis is correct. For example, if I thought climate change was warming our earth, that would be my null hypothesis. I would then need to gather data to support my null hypothesis. If I didn’t find enough data (evidence) to support the null hypothesis (claim), I would have to reject it and remain unconvinced it was likely to be true. Alternatively, if I can find enough evidence, I wouldn’t be absolutely certain of my null hypothesis; I would simply fail to reject it, which means I am convinced it is likely to be true.
Jury trials are an excellent example to help paint a clear picture. When a suspect is on trial, they are innocent until proven guilty. After reviewing the evidence, the jury returns with a guilty or not guilty verdict. There is no innocent verdict. The jury is either convinced or not convinced of the suspect’s guilt. While examining claims, we should use the same standard. When someone makes a claim, they place that claim on trial. The default position is that the claim is not true (innocent of being true). This innocent verdict is why whoever makes a claim holds the burden of proof. The claim maker must provide evidence for the jury to reach a verdict of truth (guilty of being true).
For example, let’s pretend Randy told John that Bigfoot was real. That claim (null hypothesis) is now on trial, and Randy holds the burden of proof. The existence of Bigfoot is innocent (not true) until Randy can convince John to reach a guilty (is true) verdict. Randy then provides evidence for his claim, but it’s not enough for John to be convinced. John would then reject the claim (not believe it) and return with a not-guilty verdict. This verdict doesn’t mean that John has absolute certainty that Bigfoot doesn’t exist. It means his confidence isn’t high enough to warrant a verdict of truth. The possibility of Bigfoot’s existence is still there, however small it may be, but John remains unconvinced.
Similar to jury cases, our verdict can be incorrect. This unsureness is why we should avoid absolute certainty and instead rely on our confidence level. The higher our confidence, the more convinced we can be that the claim is true. When our confidence is high enough, we ultimately reach a verdict of truth and fail to reject the claim. When we fail to reject a claim, it essentially means we believe it because we have found enough evidence to warrant belief in the claim.
We can apply this same method when looking at a range of data. Let’s revisit our bear example. Assuming we found reliable data that between 1,800 - 2,000 Grizzly bears live in Montana, let’s imagine Sharon claimed there were 2,500 bears (her null hypothesis). That claim (null hypothesis) is now on trial, and Sharon holds the burden of proof. The claim is innocent of being true until Karen can present enough evidence to convince the jury to return with a guilty verdict. If she cannot, the jury remains unconvinced, returns with a not-guilty verdict, and rejects the claim. Alternatively, if she can provide enough evidence, the jury would return with a guilty verdict and fail to reject her claim.
This process is how we should determine the likelihood that something is true. We are never absolutely certain. We are either convinced or unconvinced. It depends on our confidence in how likely it is to be true. Once again, our confidence can change with new data, and it should. We may initially reject (be unconvinced) or fail to reject a claim (be convinced), but if new evidence comes forward, we should be willing and able to change our verdict.