4 replies to this topic

[mydarling]

Hello Mycotopiates! Been a while, but this is my 4,000th post!! And to celebrate, I'm sharing with you all some information and guidelines to help propel the Myco Lab into excellence.

As explained in the forum description, the goal of this forum is to facilitate SCIENTIFIC, REPEATABLE EXPERIMENTATION leading to VERIFIED AND TESTED TEKS based on your newest and best cultivating ideas. Not just interesting ideas that are tried once and work because of luck; instead, this forum seeks to produce new innovative methods for reliable mushroom cultivation!

In order to get there, we need to understand what The Scientific Method is and agree on how to use it. Following this method is how any scientific field advances, and it can provide validity to the work being done by all you creative hobby growers out there. I think that requiring the content of the Myco Lab Forum to adhere to the scientific method will earn topia serious scientific credibility and will do a great service to the OMC.

The next few posts will outline the method, and then give you some easy examples of how to put it into action - from the simple to the more complex.

Remember to DOCUMENT EVERYTHING!

TAKE LOTS OF PICTURES!

AND BE CREATIVE!

Let’s do this, people!

Edited by TurkeyRanch, 17 December 2014 - 08:53 PM.

[mydarling]

SO, WHAT IS THE SCIENTIFIC METHOD?

The scientific method establishes a framework to collect and evaluate experimental data, by providing the necessary criteria to conduct a valid scientific experiment. This also sets up the ability for others to repeat your work, which is a very important part of research and scientific progress!

There are a few reasonably simple steps to the scientific method (some of the more technical details are being omitted).

You can use this list as your Scientific Experiment Checklist:

#1. Establish your objective.
#2a. Form a testable (measurable) hypothesis, and #2b. Explain your controls.
#3. Explain your methodology.
#4. Collect and analyze your data.
#5. Determine whether the hypothesis is supported by your data.

Now let’s take this checklist through 3 hypothetical experiments.

Edited by TurkeyRanch, 17 December 2014 - 08:54 PM.
Fixing color/quote code

[mydarling]

1st EXAMPLE: A SIMPLE, REAL-WORLD SITUATION.

Let’s put this into a more familiar context using a coin toss experiment. Say you wanted to flip a coin a bunch of times and wanted to know how many heads or tails you should expect.

#1. Establish your objective.
What are you trying to accomplish? To determine the likelihood of flipping heads (H) or tails (T).

#2a. Form a testable (measurable) hypothesis.
Your hypothesis should be based on reasonable speculation or prior knowledge/observation, and predict something that is measurable. So let’s go with this: Because a coin has 2 sides of assumed equal weight, there is an equal likelihood (50-50 chance) of getting a H or T on any given coin toss, so half of the tosses will be H and the other half will be T. [Note: Controls aren’t really applicable in this simple example, so we’ll skip #2b for now.]

#3. Explain your methodology.
Make a list of steps you will follow to conduct the experiment and collect the data. Make sure you RECORD and MEASURE ALL PARAMETERS. So: A coin will be tossed 10 times in a row using the same flipping technique, with the number of H and T being tallied.

#4. Collect and analyze your data. You perform 10 coin tosses and your data indicates results of 8H / 2T.

#5. Determine whether the hypothesis is supported by your data.
This requires a critical evaluation of the data, the methodology, and any other factors that may have played a role in this experimental outcome. Ok, so remember we hypothesized a 50-50 chance, thus we predicted 5H / 5T from 10 tosses. Clearly, our 8H / 2T data does NOT support the hypothesis. But, does this mean that we throw the hypothesis out and there is actually NOT a 50-50 H/T chance in a coin toss????

We cannot say for certain at this point, because the sample size is too small. We can’t say whether 8H / 2T was simply due to CHANCE (a recognized contributor to all experiments), or if in fact the 50-50 hypothesis is wrong … Maybe the coin actually was weighted on one side, or maybe the coin was being flipped in a biased manner? So what do we do?

We need to COLLECT MORE DATA in order to gain confidence about the meaning of our 8H / 2T results.

If we increased the number of tosses per experiment to 50, and repeated that experiment 100 separate times, the data would likely converge on an average 50-50 ratio of H/T, and you would collect data that supports your hypothesis.

But if you did this and STILL got an average 8H / 2T ratio – THEN we have enough evidence to reject the hypothesis (since you wouldn’t maintain this ratio simply due to chance with that high level of repetition).

This highlights an important point: REPITITION IS KEY. You should include enough SAMPLES (# of flips per experiment, or in the case of mushrooms, # of bags/jars/tubs/etc.) and enough REPLICATION (# of times the experiment is repeated by you or others) so that you have enough data to eliminate “chance” or “luck” as the MOST LIKELY explanation for your results.

The statistical reasoning behind this is complicated, but trust me – MORE IS BETTER!!!

Edited by TurkeyRanch, 17 December 2014 - 08:54 PM.
Fixing code

[mydarling]

2nd EXAMPLE: A SIMPLE MYCO-RELATED EXPERIMENT.

Now, let’s take these basic ideas and apply them to cultivation! (Please note, this is a hypothetical experiment and I haven’t actually done this!!! But it would be totally awesome if someone did …..)

Let’s say you wanted to experiment with the use of diatomaceous earth (DE) in PF cakes. You’re curious about the water-retaining properties of DE as a BRF additive, as compared to the vermiculite in typical PF cake.

Our first simple experiment would be to address the basic underlying assumptions: Can DE in fact retain water, and how does it compare to the water-retaining properties of verm? Let’s lay out the steps.

#1. Establish your objective.
To determine the water-retaining properties of DE as compared to verm.

#2a. Form a testable (measurable) hypothesis.
We’re talking about water content here, so our hypothesis needs to predict something based on the amount of water in these 2 materials. However, “amount” of DE/verm/water does not have an established measurement system and thus cannot really be measured, but “weight” of DE/verm/water (grams, ounces, etc.) CAN be measured! (NOTE: If you’re used to measuring things by volume, simply measure out your preferred volume and weigh it the first time, then you’ll know for future reference how much that “amount” weighs. Using weight is much more reliable than volume.)

So let’s phrase the hypothesis as follows: Because of its water-retaining properties, a given weight of DE will be able to retain a greater weight of water than an equal weight of verm.

#2b. Explain your controls.
This is very, very important. We skipped it in the Coin Toss because it that was an oversimplified experiment. But for every experiment you do in cultivation, you’ll want to follow this rule. Why? In order to tell if DE is, in fact, capable of retaining “more” water, we need to know what the “normal” amount of water is so we have a baseline with which to compare it! It’s simply impossible to know if your new, untested condition makes any difference, unless you know how the original condition behaves in identical circumstances.

• So, let’s establish the control (baseline/normal) condition: A weighed amount of verm with water added to reach field capacity.
• And then, the experimental (new/untested) condition: An equal weighed amount of DE with water added to reach field capacity.

Also, recall that you can’t be certain about the meaning of your results if you don’t have a large enough sample size. With a small sample size or a single experiment, whatever result you got could have been simply due to luck! You must increase your number of samples/replicates to decrease the influence of “chance” in your experiment.

So, let’s say you’ll do 10 samples each of hydrated DE and verm, and then repeat that whole experiment twice.

#3. Explain your methodology, and #4. Collect and analyze your data.
It is important to record all of your steps (no matter how unimportant they seem) so that no new variables are introduced. We are testing ONLY the difference in water-retaining properties of DE and verm, so we must keep all other variables identical: weight of DE, weight of verm, criteria for achieving “field capacity” hydration, etc. Please note that it doesn’t matter what method you use for your own personal experiment, as long as you MEASURE and RECORD everything and keep all parameters consistent for all of your samples!

Let’s propose these steps for our hypothetical experiment:

• Weigh out X grams of DE on a scale and tare it. Add water in Y-gram increments until field capacity is reached. RECORD the final weight = the amount of water added. Repeat this for 10 separate batches of X grams DE. Average the 10 data points together.
• Repeat Step A, but with verm instead of DE.
• Replicate the entire experiment (Steps A + B) with 10 new samples of each additive. This could be done by another experimenter.

#5. Determine whether the hypothesis is supported by your data.
So, we hypothesized that given an equal weight of DE and verm, the DE will be able to hold more water at field capacity than verm. What did we find?

• If the average water content of DE is HIGHER than verm, then YES, we have data to support this hypothesis!
• If however it is LOWER THAN or THE SAME AS verm, then NO, we do not support the hypothesis, and since we have repeated this enough times to eliminate “chance” as the reason for this, we have to reject the hypothesis or perform a different type of experiment to address the question.

So, this is pretty easy, right? It actually takes a lot more words to explain what we’re doing than the time it takes to get it done!!!

Edited by TurkeyRanch, 17 December 2014 - 08:54 PM.
Fixing code

[mydarling]

3rd EXAMPLE: A MORE COMPLEX CULTIVATION EXPERIMENT. (again, hypothetical)

Ok, let’s say your experiment above supported the fact that DE holds more water than verm. Now you want to take it to the actual cultivation step, so you need a new experiment with a new hypothesis.

#1. Establish your objective. What is it that you’re trying to establish? The objective is to determine whether DE is a suitable substrate additive in place of verm. Because we know it will hold more water than verm, we suspect that it might therefore retain more moisture in a final sterilized, colonized mushroom substrate than verm, which would of course provide the moisture needed to produce lots of fruiting bodies. So, we’d like to test whether DE can be used instead of verm in PF cakes.

#2a. Form a testable (measurable) hypothesis. Ok, so if it’s a “suitable” alternative, it has to be better for some reason – ease of use, cost or availability of materials, better mushroom production, etc. Let’s choose “better production” as our basis for investigating DE as an alternative to verm.

Remember that a hypothesis must predict something that can be measured objectively. A hypothesis that predicts “better mushroom production” is NOT testable - “production” has no established measurement system, can mean different things to different people, and the degree of “productivity” can often be a subjective decision. So, refine your hypothesis to evaluate something measurable that relates to production: how about, average total dry weight yield per substrate weight?

Now you can state your hypothesis: Because of the higher observed water-retaining properties of DE, a PF cake substrate made with DE in place of verm will produce, on average, a higher dry weight of harvested mushrooms per substrate weight, when compared to the typical PF cake containing verm.

#2b. Explain what type of controls you will use. Remember we need our baseline to determine whether this new, untested treatment really makes a difference. We need to compare it to the “normal” method when tested under identical conditions.

• So let’s establish the control (baseline/normal) condition: X grams of BRF + Y grams of verm @ field capacity.

• And then, the experimental (new/untested) condition: X grams of BRF + Y grams of of DE @ field capacity.

• You could also add in a negative control condition: X grams of BRF @ field capacity, no DE or verm added. (this allows you to see how BRF performs without any additives)

(Note that X and Y are variables; you can choose whatever values you want for X and Y, as long as they are constant across all conditions.)

Now, how many jars of each should you do??? Just 1 of each is surely not enough to remove chance from the equation. Let’s go with 5 jars of each condition (experimental, control, and negative control). And then, let’s ask that this entire experiment be repeated at least twice.

#3. Explain your methodology. Establish a strictly controlled method that will allow you and others to repeat, tweak, and perfect the various aspects of the experiment. Again, it doesn’t matter what method you use for your own personal experiment, as long as you MEASURE and RECORD everything and keep all parameters consistent for all of your samples! You should refer to a tek to get the details for your chosen method, and then tweak it to address your hypothesis. I won’t provide all the details of any TEK here since that’s not the point of this thread

Let’s propose the following hypothetical method:

 

a). Prepare basic BRF. Make sure that the texture/moisture/additive content of the final substrate is consistent for ALL conditions. Once each substrate type is mixed, place a weighed amount of completed substrate into each jar. Record the following:

• WEIGH the amount of BRF used to prepare the mix.
• WEIGH the amount of additive (DE or verm) + water for each condition.
• WEIGH the amount of completed substrate placed into each jar.
• NUMBER AND LABEL your jars so you know which is which !!!

Notice that everything is WEIGHED (not measured by volume) for consistency!

b). Sterilize and cool all prepped jars in an identical manner, in the same PC run if possible.

• RECORD the PSI on your PC, and TIME how long you maintained that pressure to sterilize.
• TIME how long the jars sat between completion of the PC run and when you actually inoculated them.

c). Inoculate the jars. Let’s say we’re using an LC prepared with tissue from an isolate. But you can use any isolate inoculum you want (slurry, etc.). *** NOTE: DO NOT USE MULTISPORE INOCULUM FOR A CONTROLLED EXPERIMENT, UNLESS YOU SPECIFICALLY WANT TO TEST A HYPOTHESIS RELATED TO GENETIC DIVERSITY. Using multispore introduces way too many uncontrolled variables and prevents you from gleaning anything meaningful from your results. ***

• HOMOGENIZE the inoculum first.
• MEASURE how much goes into each jar (be consistent).

d). Incubate the jars. The conditions (temperature, humidity, lighting, airflow, etc.) must be kept nearly identical for ALL of your jars. If any of these conditions fluctuate between the experimentals and controls, you’re introducing a whole new set of variables and, just like using multispore, it prevents you from gleaning anything meaningful from your results. However you decide to incubate, just be consistent!

• RECORD the temperature and humidity of the incubator/chamber and any other important things such as day/night fluctuations in temp, lighting, airflow, etc.
• TIME how long it takes the jars to fully colonize once introduced to the incubator.

e). Fruit the jars. Again, keep your conditions the same for all the jars!! Record your parameters … etc.

#4. Collect and analyze your data. THE FUN PART – RESULTS!!! Since you’ll recall we are measuring dry weight of harvested mushrooms per substrate weight, we must harvest fruits, dry them to standard “cracker dry” state, and weigh them - from EACH jar. You have 5 jars from each condition, so average the dry weight from those 5 jars to find the average dry harvest weight for the condition.

You might have noticed that “harvest per substrate” is a ratio, so you need to compare the average weight of the HARVEST to the average weight of the SUBSTRATE. Why did we set up this way? Because a substrate that is larger will naturally produce greater harvest, so we need to eliminate this bias from our results. Aren’t you glad you weighed the amount of substrate going into each of those jars in Step 3A????

(Note: This ratio is needed only because our hypothesis suggests a ratio. Just make sure the format of your results matches whatever parameter you included in your hypothesis!)

So, divide the average weight of the dry harvest for that condition by the average weight of the substrate in those jars.

Example:

• Experimental Cakes = 12g dry (avg) harvested from 150g (avg) cakes = [8g] / [150g] = 0.080

• Control Cakes = 8g dry (avg) harvested from 145g (avg) cakes = [8g] / [145g] = 0.055

• Negative Control Cakes = 4g dry (avg) harvested from 140g (avg) cakes = [4g] / [140g] = 0.029

Notice our results have no units (they cancel out). This allow us to compare the harvest per substrate simply by looking at the final ratio value we calculate.

#5. Determine whether the hypothesis is supported by your data. Recall our hypothesis: Because of the higher observed water-retaining properties of DE, a PF cake substrate made with DE in place of verm will produce, on average, a higher dry weight of harvested mushrooms per substrate weight, as compared to the typical PF cake containing verm.

So, did it actually do that???

We can see that the experimental cakes have a higher ratio of harvest to substrate weight (0.080) than the control cakes (0.055). In other words, the experimental condition w/ DE produces MORE harvest than the control w/ verm, given the same weight of substrate. Our data also shows that any additive (0.080 or 0.055) is better than NO additive (0.029), which is a nice supporting piece of information.

So, the conclusion: YES, our data supports the hypothesis, and YES, DE produces a higher harvest yield than the conventional verm in PF cakes, under the specific conditions tested.

HOORAY! Now it’s time to repeat the experiment and publish this as an accepted new TEK !!!!!

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If you’ve managed to get this far, hopefully you can appreciate the value of this method in facilitating transparency and repetition. EVEN FAILED EXPERIMENTS ARE WORTH DOCUMENTING!!!! For instance, if the results in the last experiment had NOT supported the hypothesis, other experimenters could then tweak/refine/improve aspects of your method. Once it has been hashed out, the final methodology can be published as a “TEK,” and then other people can simply follow the TEK to get great results – thanks to all your hard work!!!!

 

Edited by Bombadil, 18 December 2014 - 11:13 PM.
Fixing code