diff --git a/live_coding_sessions/power_analysis.jl b/live_coding_sessions/power_analysis.jl
new file mode 100644
index 0000000..35f7696
--- /dev/null
+++ b/live_coding_sessions/power_analysis.jl
@@ -0,0 +1,100 @@
+using DataFrames
+using MixedModels
+using MixedModelsSim
+using Random
+using Statistics
+using SMLP2024: dataset
+
+fm1 = fit(MixedModel, 
+          @formula(reaction ~ 1 + days + (1 + days|subj)),
+          dataset(:sleepstudy))
+
+parametricbootstrap(MersenneTwister(42), 1000, fm1)       
+
+slpsim = DataFrame(dataset(:sleepstudy); copycols=true)
+
+slpsim[:, :reaction] .= 0.0
+
+slpsimmod = LinearMixedModel(@formula(reaction ~ 1 + days + (1 + days|subj)), slpsim)
+
+# coefnames(slpsimmod)
+simulate!(MersenneTwister(42), slpsimmod; β=[500, 50], σ=250)
+response(slpsimmod)
+fit!(slpsimmod)
+
+slpsimpow = parametricbootstrap(MersenneTwister(42), 1000, slpsimmod; β=[500, 50], σ=250)
+
+combine(groupby(DataFrame(slpsimpow.coefpvalues), :coefname),
+        :p => (p -> mean(<(0.05), p)) => :power)    
+
+# now let's do random effects!
+# these are expressed _relative_ to the residual standard deviation
+# so we devide by 250, which is what we set as the residual standard deviation
+# TODO: add a named argument "relative=true"     
+subj_re = create_re(100/250, 20/250)
+update!(slpsimmod; subj=subj_re)
+
+simulate!(MersenneTwister(42), slpsimmod; β=[500, 50], σ=250, θ=slpsimmod.θ)
+fit!(slpsimmod)
+
+slpsimpow = parametricbootstrap(MersenneTwister(42), 1000, slpsimmod; β=[500, 50], σ=250)
+
+combine(groupby(DataFrame(slpsimpow.coefpvalues), :coefname),
+        :p => (p -> mean(<(0.05), p)) => :power)   
+
+
+# big questions:
+# where do we get these numbers from?
+
+# holding the RE and residual and FE-intercept constant, what is the smallest 
+# days-effect that I could detect with the given sample size? 
+
+to_consider = [10, 20, 30, 50]
+# initialize an empty array of dataframes
+considerations = DataFrame[]
+for eff_size in to_consider
+    slpsimpow = parametricbootstrap(MersenneTwister(42), 1000, slpsimmod; 
+                                     β=[500, eff_size], σ=250)
+    power_at_size = combine(groupby(DataFrame(slpsimpow.coefpvalues), :coefname),
+                             :p => (p -> mean(<(0.05), p)) => :power) 
+    power_at_size[:, :eff_size] .= eff_size
+    push!(considerations, power_at_size)
+end
+
+# put it all together in one dataframe
+power = reduce(vcat, considerations)
+ 
+# for a given sample size, now we know what size effects we can detect
+# what happens if we have an effect size and what to know what sample size we need?
+
+# for this study, there are no items
+# and the number of observations per subject is given by the design
+# so we only have one n to worry about
+days = 0:9 
+rng = MersenneTwister(42)
+subj_re = create_re(100/250, 20/250)
+
+considerations = DataFrame[]
+for n_subj in [10, 20, 30, 40]
+    subj_ids = "S" .* lpad.(string.(1:n_subj), 3, '0')
+
+    subj_data = DataFrame[]
+    for subj in subj_ids
+        sdat = DataFrame(; subj, days)
+        push!(subj_data, sdat)
+    end
+    simdat = reduce(vcat, subj_data)
+    # initialize the response with normally distributed noise
+    simdat[:, :reaction] .= randn(nrow(simdat))
+
+    slpsimmod = LinearMixedModel(@formula(reaction ~ 1 + days + (1 + days|subj)), simdat)
+   
+    update!(slpsimmod; subj=subj_re)
+    slpsimpow = parametricbootstrap(rng, 1000, slpsimmod; β=[500, 10], σ=250)
+    power_at_size = combine(groupby(DataFrame(slpsimpow.coefpvalues), :coefname),
+                             :p => (p -> mean(<(0.05), p)) => :power) 
+    power_at_size[:, :n_subj] .= n_subj
+    push!(considerations, power_at_size)
+end
+
+power = reduce(vcat, considerations)